Patent Publication Number: US-2022222355-A1

Title: Entity Behavior Catalog Architecture

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates in general to the field of computers and similar technologies, and in particular to software utilized in this field. Still more particularly, it relates to a method, system and computer-usable medium for cataloging entity behavior. 
     Description of the Related Art 
     Users interact with physical, system, data, and services resources of all kinds, as well as each other, on a daily basis. Each of these interactions, whether accidental or intended, poses some degree of security risk, depending on the behavior of the user. In particular, the actions of a formerly trusted user may become malicious as a result of being subverted, compromised or radicalized due to any number of internal or external factors or stressors. For example, financial pressure, political idealism, irrational thoughts, or other influences may adversely affect a user&#39;s intent and/or behavior. 
     SUMMARY OF THE INVENTION 
     In one embodiment the invention relates to a method for cataloging entity behavior, comprising: identifying a security related activity, the security related activity being based upon an observable from an electronic data source; analyzing the security related activity, the analyzing identifying an event of analytic utility associated with the security related activity; generating entity behavior catalog data based upon the event of analytic utility associated with the security related activity; and, storing the entity behavior catalog data within an entity behavior catalog, the entity behavior catalog providing an inventory of entity behaviors for use when performing a security operation. 
     In another embodiment the invention relates to a system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: identifying a security related activity, the security related activity being based upon an observable from an electronic data source; analyzing the security related activity, the analyzing identifying an event of analytic utility associated with the security related activity; generating entity behavior catalog data based upon the event of analytic utility associated with the security related activity; and, storing the entity behavior catalog data within an entity behavior catalog, the entity behavior catalog providing an inventory of entity behaviors for use when performing a security operation. 
     In another embodiment the invention relates to a computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: identifying a security related activity, the security related activity being based upon an observable from an electronic data source; analyzing the security related activity, the analyzing identifying an event of analytic utility associated with the security related activity; generating entity behavior catalog data based upon the event of analytic utility associated with the security related activity; and, storing the entity behavior catalog data within an entity behavior catalog, the entity behavior catalog providing an inventory of entity behaviors for use when performing a security operation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention may be better understood, and its numerous objects, features and advantages made apparent to those skilled in the art by referencing the accompanying drawings. The use of the same reference number throughout the several figures designates a like or similar element. 
         FIG. 1  depicts an exemplary client computer in which the present invention may be implemented; 
         FIG. 2  is a simplified block diagram of an edge device; 
         FIG. 3  is a simplified block diagram of an endpoint agent; 
         FIG. 4  is a simplified block diagram of a security analytics system; 
         FIG. 5  is a simplified block diagram of a security analytics system; 
         FIGS. 6 a  and 6 b    show a simplified block diagram of an entity behavior profile (EBP) and a prepopulated EBP; 
         FIGS. 7 a  and 7 b    show a block diagram of a security analytics system environment; 
         FIG. 8  is a simplified block diagram showing the mapping of an event to a security vulnerability scenario; 
         FIG. 9  is a simplified block diagram of the generation of a session and a corresponding session-based fingerprint; 
         FIG. 10  is simplified block diagram of process flows associated with the operation of an entity behavior catalog (EBC) system; 
         FIG. 11  is a generalized flowchart of the performance of session-based fingerprint generation operations; 
         FIG. 12  is a table showing components of an EBP; 
         FIG. 13  is an activities table showing analytic utility actions occurring during a session; 
         FIG. 14  shows a simplified block diagram of the components of a cyber kill chain associated with the performance of a security operation; 
         FIGS. 15 a  and 15 b    are a generalized flowchart of the performance of EBP definition and management operations; 
         FIG. 16  shows a functional block diagram of the operation of an EBC system; 
         FIGS. 17 a  and 17 b    are a simplified block diagram showing components of an EBC system; 
         FIG. 18  is a simplified block diagram showing the mapping of entity behaviors to a risk use case scenario; 
         FIG. 19  is a simplified block diagram of an EBC system environment; 
         FIG. 20  is a simplified block diagram of an EBC system used to modify an existing, or generate a new EBP; 
         FIGS. 21 a  through 21 c    are a generalized flowchart of the performance of EBP system operations to generate a new, or modify an existing, EBP for an associated entity; and 
         FIGS. 22 a  through 22 d    are a generalized flowchart of the performance of EBP system operations to generate a new, or modify an existing, prepopulated EBP. 
     
    
    
     DETAILED DESCRIPTION 
     A method, system and computer-usable medium are disclosed for cataloging entity behavior. Certain aspects of the invention include an appreciation that the existence of any entity, whether it is an individual user, a group of users, an organization, a device, a system, a network, an account, a domain, an operation, a process, a software application, or a service, represents some degree of security risk. Various aspects of the invention likewise include an appreciation that certain non-user entities, such as computing, communication, and surveillance devices can be a source for telemetry associated with certain events and entity behaviors. Likewise, various aspects of the invention include an appreciation that certain accounts may be global, spanning multiple devices, such as a domain-level account allowing an entity access to multiple systems. Certain aspects of the invention likewise include an appreciation that a particular account may be shared by multiple entities. 
     Accordingly, certain aspects of the invention include an appreciation that a particular entity can be assigned a measure of risk according to its respective attributes, behaviors, associated behavioral models, and resultant inferences contained in an associated profile. As an example, a first profile may have an attribute that its corresponding entity works in the human resource department, while a second profile may have an attribute that its corresponding entity is an email server. To continue the example, the first profile may have an associated behavior that indicates its corresponding entity is not acting as they did the day before, while the second profile may have an associated behavior that indicates its corresponding entity is connecting to a suspicious IP address. To further continue the example, the first profile may have a resultant inference that its corresponding entity is likely to be leaving the company, while the second profile may have a resultant inference that there is a high probability its corresponding entity is compromised. Accordingly, certain embodiments of the invention include an appreciation that a catalog of such behaviors, and associated profiles, can assist in identifying entity behavior that may be of analytic utility. Likewise, certain embodiments of the invention include an appreciation that such entity behavior of analytic utility may be determined to be anomalous, abnormal, unexpected, malicious, or some combination thereof, as described in greater detail herein. 
     For the purposes of this disclosure, an information handling system may include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, entertainment, or other purposes. For example, an information handling system may be a personal computer, a mobile device such as a tablet or smartphone, a consumer electronic device, a connected “smart device,” a network appliance, a network storage device, a network gateway device, a server or collection of servers or any other suitable device and may vary in size, shape, performance, functionality, and price. The information handling system may include volatile and/or non-volatile memory, and one or more processing resources such as a central processing unit (CPU) or hardware or software control logic. Additional components of the information handling system may include one or more storage systems, one or more wired or wireless interfaces for communicating with other networked devices, external devices, and various input and output (I/O) devices, such as a keyboard, a mouse, a microphone, speakers, a track pad, a touchscreen and a display device (including a touch sensitive display device). The information handling system may also include one or more buses operable to transmit communication between the various hardware components. 
     For the purposes of this disclosure, computer-readable media may include any instrumentality or aggregation of instrumentalities that may retain data and/or instructions for a period of time. Computer-readable media may include, without limitation, storage media such as a direct access storage device (e.g., a hard disk drive or solid state drive), a sequential access storage device (e.g., a tape disk drive), optical storage device, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and/or flash memory; as well as communications media such as wires, optical fibers, microwaves, radio waves, and other electromagnetic and/or optical carriers; and/or any combination of the foregoing. 
       FIG. 1  is a generalized illustration of an information handling system  100  that can be used to implement the system and method of the present invention. The information handling system  100  includes a processor (e.g., central processor unit or “CPU”)  102 , input/output (I/O) devices  104 , such as a display, a keyboard, a mouse, and associated controllers, a storage system  106 , and various other subsystems  108 . In various embodiments, the information handling system  100  also includes network port  110  operable to connect to a network  140 , which is likewise accessible by a service provider server  142 . The information handling system  100  likewise includes system memory  112 , which is interconnected to the foregoing via one or more buses  114 . System memory  112  further includes operating system (OS)  116  and in various embodiments may also include a security analytics system  118 . In one embodiment, the information handling system  100  is able to download the security analytics system  118  from the service provider server  142 . In another embodiment, the security analytics system  118  is provided as a service from the service provider server  142 . 
     In various embodiments, the security analytics system  118  performs a security analytics operation. In certain embodiments, the security analytics operation improves processor efficiency, and thus the efficiency of the information handling system  100 , by facilitating security analytics functions. As will be appreciated, once the information handling system  100  is configured to perform the security analytics operation, the information handling system  100  becomes a specialized computing device specifically configured to perform the security analytics operation and is not a general purpose computing device. Moreover, the implementation of the security analytics system  118  on the information handling system  100  improves the functionality of the information handling system  100  and provides a useful and concrete result of performing security analytics functions to mitigate security risk. 
     In certain embodiments, the security analytics system  118  may be implemented to include an entity behavior catalog (EBC) system  120 . In certain embodiments, the EBC system  120  may be implemented to catalog entity behavior, as described in greater detail herein. In certain embodiments, the EBC system  120  may likewise be implemented to include an EBC access management  122  module, an EBP management  124  module, a security vulnerability scenario management  126  module, and a security risk use case management  128  module, or a combination thereof. 
     In various embodiments, the EBC access management  122  module may be implemented to perform certain EBC access management operations, as described in greater detail herein. In various embodiments, the EBP management  124  module may be implemented to perform certain EBP management operations, as likewise described in greater detail herein. Likewise, the security vulnerability scenario management  126  module may be implemented in various embodiments to perform certain security vulnerability scenario management operations, as described in greater detail herein. As likewise described in greater detail herein, the security risk use case management  128  module may be implemented in various embodiments to perform certain security risk use case management operations. 
       FIG. 2  is a simplified block diagram of an edge device implemented in accordance with an embodiment of the invention. As used herein, an edge device, such as the edge device  202  shown in  FIG. 2 , broadly refers to a device providing an entry point into a network  140 . Examples of such edge devices  202  may include routers, routing switches, integrated access devices (IADs), multiplexers, wide-area network (WAN) access devices, and network security appliances. In certain embodiments, the network  140  may be a private network (e.g., an enterprise network), a semi-public network (e.g., a service provider core network), or a public network (e.g., the Internet). 
     Skilled practitioners of the art will be aware that edge devices  202  are often implemented as routers that provide authenticated access to faster, more efficient backbone and core networks. Furthermore, current industry trends include making edge devices  202  more intelligent, which allows core devices to operate at higher speed as they are not burdened with additional administrative overhead. Accordingly, such edge devices  202  often include Quality of Service (QoS) and multi-service functions to manage different types of traffic. Consequently, it is common to design core networks with switches that use routing protocols such as Open Shortest Path First (OSPF) or Multiprotocol Label Switching (MPLS) for reliability and scalability. Such approaches allow edge devices  202  to have redundant links to the core network, which not only provides improved reliability, but enables enhanced, flexible, and scalable security capabilities as well. 
     In certain embodiments, the edge device  202  may be implemented to include a communications/services architecture  204 , various pluggable capabilities  212 , a traffic router  210 , and a pluggable hosting framework  208 . In certain embodiments, the communications/services architecture  202  may be implemented to provide access to and from various networks  140 , cloud services  206 , or a combination thereof. In certain embodiments, the cloud services  206  may be provided by a cloud infrastructure familiar to those of skill in the art. In certain embodiments, the edge device  202  may be implemented to provide support for a variety of generic services, such as directory integration, logging interfaces, update services, and bidirectional risk/context flows associated with various analytics. In certain embodiments, the edge device  202  may be implemented to provide temporal information, described in greater detail herein, associated with the provision of such services. 
     In certain embodiments, the edge device  202  may be implemented as a generic device configured to host various network communications, data processing, and security management capabilities. In certain embodiments, the pluggable hosting framework  208  may be implemented to host such capabilities in the form of pluggable capabilities  212 . In certain embodiments, the pluggable capabilities  212  may include capability ‘1’  214  (e.g., basic firewall), capability ‘2’  216  (e.g., general web protection), capability ‘3’  218  (e.g., data sanitization), and so forth through capability ‘n’  220 , which may include capabilities needed for a particular operation, process, or requirement on an as-needed basis. In certain embodiments, such capabilities may include the performance of operations associated with managing an adaptive trust Profile (ATP), described in greater detail herein. In certain embodiments, such operations may include the provision of associated temporal information (e.g., time stamps). 
     In certain embodiments, the pluggable capabilities  212  may be sourced from various cloud services  206 . In certain embodiments, the pluggable hosting framework  208  may be implemented to provide certain computing and communication infrastructure components, and foundation capabilities, required by one or more of the pluggable capabilities  212 . In certain embodiments, the pluggable hosting framework  208  may be implemented to allow the pluggable capabilities  212  to be dynamically invoked. Skilled practitioners of the art will recognize that many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
       FIG. 3  is a simplified block diagram of an endpoint agent implemented in accordance with an embodiment of the invention. As used herein, an endpoint agent  306  broadly refers to a software agent used in combination with an endpoint device  304  to establish a protected endpoint  302 . Skilled practitioners of the art will be familiar with software agents, which are computer programs that perform actions on behalf of a user or another program. In various approaches, a software agent may be autonomous or work together with another agent or a user. In certain of these approaches the software agent is implemented to autonomously decide if a particular action is appropriate for a given event, such as an observed entity behavior. 
     An endpoint device  304 , as likewise used herein, refers to an information processing system such as a personal computer, a laptop computer, a tablet computer, a personal digital assistant (PDA), a smart phone, a mobile telephone, a digital camera, a video camera, or other device capable of storing, processing and communicating data. In certain embodiments, the communication of the data may take place in real-time or near-real-time. As used herein, real-time broadly refers to processing and providing information within a time interval brief enough to not be discernable by a user. As an example, a cellular phone conversation may be used to communicate information in real-time, while an instant message (IM) exchange may be used to communicate information in near real-time. In certain embodiments, the communication of the information may take place asynchronously. For example, an email message may be stored on an endpoint device  304  when it is offline. In this example, the information may be communicated to its intended recipient once the endpoint device  304  gains access to a network  140 . 
     A protected endpoint  302 , as likewise used herein, broadly refers to a policy-based approach to network security that typically requires endpoint devices  304  to comply with certain criteria before they are granted access to network resources. As an example, a given endpoint device  304  may be required to have a particular operating system (OS), or version thereof, a Virtual Private Network (VPN) client, anti-virus software with current updates, and so forth. In certain embodiments, the protected endpoint  302  may be implemented to perform operations associated with providing real-time resolution of the identity of an entity at a particular point in time, as described in greater detail herein. In certain embodiments, the protected endpoint  302  may be implemented to provide temporal information, such as timestamp information, associated with such operations. 
     In certain embodiments, the real-time resolution of the identity of an entity at a particular point in time may be based upon contextual information associated with a given entity behavior. As used herein, contextual information broadly refers to any information, directly or indirectly, individually or in combination, related to a particular entity behavior. In certain embodiments, entity behavior may include an entity&#39;s physical behavior, cyber behavior, or a combination thereof. As likewise used herein, physical behavior broadly refers to any entity behavior occurring within a physical realm. More particularly, physical behavior may include any action enacted by an entity that can be objectively observed, or indirectly inferred, within a physical realm. 
     As an example, a user may attempt to use an electronic access card to enter a secured building at a certain time. In this example, the use of the access card to enter the building is the action and the reading of the access card makes the user&#39;s physical behavior electronically-observable. As another example, a first user may physically transfer a document to a second user, which is captured by a video surveillance system. In this example, the physical transferal of the document from the first user to the second user is the action. Likewise, the video record of the transferal makes the first and second user&#39;s physical behavior electronically-observable. As used herein, electronically-observable entity behavior broadly refers to any behavior exhibited or enacted by an entity that can be electronically observed. 
     Cyber behavior, as used herein, broadly refers to any behavior occurring in cyberspace, whether enacted by an individual user, a group of users, or a system acting at the behest of an individual user, a group of users, or an entity. More particularly, cyber behavior may include physical, social, or mental actions that can be objectively observed, or indirectly inferred, within cyberspace. As an example, a user may use an endpoint device  304  to access and browse a particular website on the Internet. In this example, the individual actions performed by the user to access and browse the website constitute a cyber behavior. As another example, a user may use an endpoint device  304  to download a data file from a particular system at a particular point in time. In this example, the individual actions performed by the user to download the data file, and associated temporal information, such as a time-stamp associated with the download, constitute a cyber behavior. In these examples, the actions are enacted within cyberspace, in combination with associated temporal information, which makes them electronically-observable. 
     As likewise used herein, cyberspace broadly refers to a network  140  environment capable of supporting communication between two or more entities. In certain embodiments, the entity may be a user, an endpoint device  304 , or various resources, described in greater detail herein. In certain embodiments, the entities may include various endpoint devices  304  or resources operating at the behest of an entity, such as a user. In certain embodiments, the communication between the entities may include audio, image, video, text, or binary data. 
     As described in greater detail herein, the contextual information may include a user&#39;s authentication factors. Contextual information may likewise include various temporal identity resolution factors, such as identification factors associated with the entity, the date/time/frequency of various entity behaviors, the entity&#39;s location, the entity&#39;s role or position in an organization, their associated access rights, and certain user gestures employed by a user in the enactment of a user behavior. Other contextual information may likewise include various user interactions, whether the interactions are with an endpoint device  304 , a network  140 , a resource, or another user. In certain embodiments, entity behaviors, and their related contextual information, may be collected at particular points of observation, and at particular points in time, described in greater detail herein. In certain embodiments, a protected endpoint  302  may be implemented as a point of observation for the collection of entity behavior and contextual information. 
     In certain embodiments, the endpoint agent  306  may be implemented to universally support a variety of operating systems, such as Apple Macintosh®, Microsoft Windows®, Linux®, Android® and so forth. In certain embodiments, the endpoint agent  306  may be implemented to interact with the endpoint device  304  through the use of low-level hooks  312  at the operating system level. It will be appreciated that the use of low-level hooks  312  allows the endpoint agent  306  to subscribe to multiple events through a single hook. Consequently, multiple functionalities provided by the endpoint agent  306  can share a single data stream, using only those portions of the data stream they may individually need. Accordingly, system efficiency can be improved and operational overhead reduced. 
     In certain embodiments, the endpoint agent  306  may be implemented to provide a common infrastructure for pluggable feature packs  308 . In various embodiments, the pluggable feature packs  308  may provide certain security management functionalities. Examples of such functionalities may include various anti-virus and malware detection, data loss protection (DLP), insider threat detection, and so forth. In certain embodiments, the security management functionalities may include one or more functionalities associated with providing real-time resolution of the identity of an entity at a particular point in time, as described in greater detail herein. 
     In certain embodiments, a particular pluggable feature pack  308  is invoked as needed by the endpoint agent  306  to provide a given functionality. In certain embodiments, individual features of a particular pluggable feature pack  308  are invoked as needed. It will be appreciated that the ability to invoke individual features of a pluggable feature pack  308 , without necessarily invoking all such features, will likely improve the operational efficiency of the endpoint agent  306  while simultaneously reducing operational overhead. Accordingly, the endpoint agent  306  can self-optimize in certain embodiments by using the common infrastructure and invoking only those pluggable components that are applicable or needed for a given user behavior. 
     In certain embodiments, the individual features of a pluggable feature pack  308  are invoked by the endpoint agent  306  according to the occurrence of a particular user behavior. In certain embodiments, the individual features of a pluggable feature pack  308  are invoked by the endpoint agent  306  according to the occurrence of a particular temporal event, described in greater detail herein. In certain embodiments, the individual features of a pluggable feature pack  308  are invoked by the endpoint agent  306  at a particular point in time. In these embodiments, the method by which a given user behavior, temporal event, or point in time is selected is a matter of design choice. 
     In certain embodiments, the individual features of a pluggable feature pack  308  may be invoked by the endpoint agent  306  according to the context of a particular user behavior. As an example, the context may be the user enacting the user behavior, their associated risk classification, which resource they may be requesting, the point in time the user behavior is enacted, and so forth. In certain embodiments, the pluggable feature packs  308  may be sourced from various cloud services  206 . In certain embodiments, the pluggable feature packs  308  may be dynamically sourced from various cloud services  206  by the endpoint agent  306  on an as-need basis. 
     In certain embodiments, the endpoint agent  306  may be implemented with additional functionalities, such as event analytics  310 . In certain embodiments, the event analytics  310  functionality may include analysis of various user behaviors, described in greater detail herein. In certain embodiments, the endpoint agent  306  may be implemented with a thin hypervisor  314 , which can be run at Ring −1, thereby providing protection for the endpoint agent  306  in the event of a breach. As used herein, a thin hypervisor broadly refers to a simplified, OS-dependent hypervisor implemented to increase security. As likewise used herein, Ring −1 broadly refers to approaches allowing guest operating systems to run Ring 0 (i.e., kernel) operations without affecting other guests or the host OS. Those of skill in the art will recognize that many such embodiments and examples are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
       FIG. 4  is a simplified block diagram of a security analytics system implemented in accordance with an embodiment of the invention. In certain embodiments, the security analytics system  118  shown in  FIG. 4  may include an event queue analytics  404  module, described in greater detail herein. In certain embodiments, the event queue analytics  404  sub-system may be implemented to include an enrichment  406  module and a streaming analytics  408  module. In certain embodiments, the security analytics system  118  may be implemented to provide log storage, reporting, and analytics capable of performing streaming  408  and on-demand  410  analytics operations. In certain embodiments, such operations may be associated with defining and managing an adaptive trust profile (ATP), detecting entity behavior that may be of analytic utility, adaptively responding to mitigate risk, or a combination thereof, as described in greater detail herein. In certain embodiments, entity behavior of analytic utility may be determined to be anomalous, abnormal, unexpected, malicious, or some combination thereof, as described in greater detail herein. 
     In certain embodiments, the security analytics system  118  may be implemented to provide a uniform platform for storing events and contextual information associated with various entity behaviors and performing longitudinal analytics. As used herein, longitudinal analytics broadly refers to performing analytics of entity behaviors occurring over a particular period of time. As an example, an entity may iteratively attempt to access certain proprietary information stored in various locations. In addition, the attempts may occur over a brief period of time. To continue the example, the fact that the information the entity is attempting to access is proprietary, that it is stored in various locations, and the attempts are occurring in a brief period of time, in combination, may indicate the entity behavior enacted by the entity is suspicious. As another example, certain entity identifier information (e.g., a user name) associated with an entity may change over time. In this example, a change in the entity&#39;s user name, during a particular period of time or at a particular point in time, may represent suspicious entity behavior. 
     In certain embodiments, the security analytics system  118  may be implemented to be scalable. In certain embodiments, the security analytics system  118  may be implemented in a centralized location, such as a corporate data center. In these embodiments, additional resources may be added to the security analytics system  118  as needs grow. In certain embodiments, the security analytics system  118  may be implemented as a distributed system. In these embodiments, the security analytics system  118  may span multiple information handling systems. In certain embodiments, the security analytics system  118  may be implemented in a cloud environment. In certain embodiments, the security analytics system  118  may be implemented in a virtual machine (VM) environment. In such embodiments, the VM environment may be configured to dynamically and seamlessly scale the security analytics system  118  as needed. Skilled practitioners of the art will recognize that many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
     In certain embodiments, an event stream collector  402  may be implemented to collect event and related contextual information, described in greater detail herein, associated with various entity behaviors. In these embodiments, the method by which the event and contextual information is selected to be collected by the event stream collector  402  is a matter of design choice. In certain embodiments, the event and contextual information collected by the event stream collector  402  may be processed by an enrichment module  406  to generate enriched entity behavior information. In certain embodiments, the enrichment may include certain contextual information related to a particular entity behavior or event. In certain embodiments, the enrichment may include certain temporal information, such as timestamp information, related to a particular entity behavior or event. 
     In certain embodiments, enriched entity behavior information may be provided by the enrichment module  406  to a streaming  408  analytics module. In turn, the streaming  408  analytics module may provide some or all of the enriched entity behavior information to an on-demand  410  analytics module. As used herein, streaming  408  analytics broadly refers to analytics performed in near real-time on enriched entity behavior information as it is received. Likewise, on-demand  410  analytics broadly refers herein to analytics performed, as they are requested, on enriched entity behavior information after it has been received. In certain embodiments, the enriched entity behavior information may be associated with a particular event. In certain embodiments, the enrichment  406  and streaming analytics  408  modules may be implemented to perform event queue analytics  404  operations, as described in greater detail herein. 
     In certain embodiments, the on-demand  410  analytics may be performed on enriched entity behavior associated with a particular interval of, or point in, time. In certain embodiments, the streaming  408  or on-demand  410  analytics may be performed on enriched entity behavior associated with a particular user, group of users, one or more non-user entities, or a combination thereof. In certain embodiments, the streaming  408  or on-demand  410  analytics may be performed on enriched entity behavior associated with a particular resource, such as a facility, system, datastore, or service. Those of skill in the art will recognize that many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
     In certain embodiments, the results of various analytics operations performed by the streaming  408  or on-demand  410  analytics modules may be provided to a storage Application Program Interface (API)  414 . In turn, the storage API  412  may be implemented to provide access to various datastores ‘1’  416  through ‘n’  418 , which in turn are used to store the results of the analytics operations. In certain embodiments, the security analytics system  118  may be implemented with a logging and reporting front-end  412 , which is used to receive the results of analytics operations performed by the streaming  408  analytics module. In certain embodiments, the datastores ‘1’  416  through ‘n’  418  may variously include a datastore of entity identifiers, temporal events, or a combination thereof. 
     In certain embodiments, the security analytics system  118  may include a risk scoring  420  module implemented to perform risk scoring operations, described in greater detail herein. In certain embodiments, functionalities of the risk scoring  420  module may be provided in the form of a risk management service  422 . In certain embodiments, the risk management service  422  may be implemented to perform operations associated with defining and managing an adaptive trust profile (ATP), as described in greater detail herein. In certain embodiments, the risk management service  422  may be implemented to perform operations associated with detecting entity behavior that may be of analytic utility and adaptively responding to mitigate risk, as described in greater detail herein. In certain embodiments, the risk management service  422  may be implemented to provide the results of various analytics operations performed by the streaming  406  or on-demand  408  analytics modules. In certain embodiments, the risk management service  422  may be implemented to use the storage API  412  to access various enhanced cyber behavior and analytics information stored on the datastores ‘1’  414  through ‘n’  416 . Skilled practitioners of the art will recognize that many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
       FIG. 5  is a simplified block diagram of the operation of a security analytics system implemented in accordance with an embodiment of the invention. In certain embodiments, the security analytics system  118  may be implemented to perform operations associated with detecting entity behavior that may be of analytic utility, as described in greater detail herein. In certain embodiments, the security analytics system  118  may be implemented in combination with one or more endpoint agents  306 , one or more edge devices  202 , various cloud services  206 , and a network  140  to perform such operations. 
     In certain embodiments, the network edge device  202  may be implemented in a bridge, a firewall, or a passive monitoring configuration. In certain embodiments, the edge device  202  may be implemented as software running on an information handling system. In certain embodiments, the network edge device  202  may be implemented to provide integrated logging, updating and control. In certain embodiments, the edge device  202  may be implemented to receive network requests and context-sensitive user behavior information in the form of enriched user behavior information  510 , described in greater detail herein, from an endpoint agent  306 , likewise described in greater detail herein. 
     In certain embodiments, the security analytics system  118  may be implemented as both a source and a sink of user behavior information. In certain embodiments, the security analytics system  118  may be implemented to serve requests for user/resource risk data. In certain embodiments, the edge device  202  and the endpoint agent  306 , individually or in combination, may provide certain entity behavior information to the security analytics system  118  using either push or pull approaches familiar to skilled practitioners of the art. 
     As described in greater detail herein, the edge device  202  may be implemented in certain embodiments to receive enriched user behavior information  510  from the endpoint agent  306 . It will be appreciated that such enriched user behavior information  510  will likely not be available for provision to the edge device  202  when an endpoint agent  306  is not implemented for a corresponding endpoint device  304 . However, the lack of such enriched user behavior information  510  may be accommodated in various embodiments, albeit with reduced functionality related to operations associated with defining and managing an entity profile, detecting entity behavior that may be normal or of analytic utility, mitigating associated risk, or a combination thereof. 
     In certain embodiments, a given user behavior may be enriched by an associated endpoint agent  306  attaching contextual information to a request. In certain embodiments, the context is embedded within a network request, which is then provided as enriched user behavior information  510 . In certain embodiments, the contextual information may be concatenated, or appended, to a request, which in turn may be provided as enriched user behavior information  510 . In these embodiments, the enriched user behavior information  510  may be unpacked upon receipt and parsed to separate the request and its associated contextual information. Certain embodiments of the invention reflect an appreciation that one possible disadvantage of such an approach is that it may perturb certain Intrusion Detection System and/or Intrusion Detection Prevention (IDS/IDP) systems implemented on a network  140 . 
     In certain embodiments, new flow requests may be accompanied by a contextual information packet sent to the edge device  202 . In these embodiments, the new flow requests may be provided as enriched user behavior information  510 . In certain embodiments, the endpoint agent  306  may also send updated contextual information to the edge device  202  once it becomes available. As an example, an endpoint agent  306  may share a list of files that have been read by a current process at any point in time once the information has been collected. To continue the example, such a list of files may be used to determine which data the endpoint agent  306  may be attempting to exfiltrate. 
     In certain embodiments, point analytics processes executing on the edge device  202  may request a particular service. As an example, risk scores associated with a particular event on a per-user basis may be requested. In certain embodiments, the service may be requested from the security analytics system  118 . In certain embodiments, the service may be requested from various cloud services  206 . 
     In certain embodiments, contextual information associated with a particular entity behavior may be attached to various network service requests. In certain embodiments, the request may be wrapped and then handled by proxy. In certain embodiments, a small packet of contextual information associated with an entity behavior may be sent with a service request. In certain embodiments, service requests may be related to Domain Name Service (DNS), web browsing activity, email, and so forth, all of which are essentially requests for service by an endpoint device  304 . In certain embodiments, such service requests may be associated with temporal event information, described in greater detail herein. Consequently, such requests can be enriched by the addition of entity behavior contextual information (e.g., UserAccount, interactive/automated, data-touched, temporal event information, etc.). Accordingly, the edge device  202  can then use this information to manage the appropriate response to submitted requests. 
     In certain embodiments, the security analytics system  118  may be implemented in different operational configurations. In certain embodiments, the security analytics system  118  may be implemented by using the endpoint agent  306 . In certain embodiments, the security analytics system  118  may be implemented by using endpoint agent  306  in combination with the edge device  202 . In certain embodiments, the cloud services  206  may likewise be implemented for use by the endpoint agent  306 , the edge device  202 , and the security analytics system  118 , individually or in combination. In these embodiments, the security analytics system  118  may be primarily oriented to performing risk assessment operations related to entity actions, software program actions, data accesses, or a combination thereof. In certain embodiments, software program actions may be treated as a proxy for the entity. 
     In certain embodiments, the endpoint agent  306  may be implemented to update the security analytics system  118  with user behavior and associated contextual information, thereby allowing an offload of certain analytics processing overhead. In certain embodiments, this approach allows for longitudinal risk scoring, which assesses risk associated with certain user behavior during a particular interval of time. In certain embodiments, the security analytics system  118  may be implemented to access risk scores associated with the same user account, but accrued on different endpoint devices  304 . It will be appreciated that such an approach may prove advantageous when an adversary is “moving sideways” through a network environment, using different endpoint devices  304  to collect information. 
     In certain embodiments, the security analytics system  118  may be primarily oriented to applying risk mitigations in a way that maximizes security effort return-on-investment (ROI). In certain embodiments, this approach may be accomplished by providing additional contextual and entity behavior information associated with entity requests. As an example, a web gateway may not concern itself with why a particular file is being requested by a certain entity at a particular point in time. Accordingly, if the file cannot be identified as malicious or harmless, there is no context available to determine how, or if, to proceed. To extend the example, the edge device  202  and security analytics system  118  may be coupled such that requests can be contextualized and fitted into a framework that evaluates their associated risk. Certain embodiments of the invention reflect an appreciation that such an approach works well with web-based data loss protection (DLP) approaches, as each transfer is no longer examined in isolation, but in the broader context of an identified entity&#39;s actions, at a particular time, on the network  140 . 
     As another example, the security analytics system  118  may be implemented to perform risk scoring processes to decide whether to block or allow unusual flows. In various embodiments, the risk scoring processes may be implemented to include certain aspects of eXtensible Access Control Markup Language (XACML) approaches known to skilled practitioners of the art. In certain embodiments, XACML obligations may be implemented to block or allow unusual flows. In certain embodiments, an XACML obligation may be implemented as a directive from a policy decision point (PDP) to a policy enforcement point (PEP) regarding what must be performed before or after a flow is approved. Certain embodiments of the invention reflect an appreciation that such an approach is highly applicable to defending against point-of-sale (POS) malware, a breach technique that has become increasingly more common in recent years. Certain embodiments of the invention likewise reflect an appreciation that while various edge device  202  implementations may not stop all such exfiltrations, they may be able to complicate the task for the attacker. 
     In certain embodiments, the security analytics system  118  may be primarily oriented to maximally leverage contextual information associated with various entity behaviors within the system. In certain embodiments, data flow tracking is performed by one or more endpoint agents  306 , which allows the quantity and type of information associated with particular hosts to be measured. In turn, this information may be used to determine how the edge device  202  handles requests. By contextualizing such entity behavior on the network  140 , the security analytics system  118  can provide intelligent protection, making decisions that make sense in the broader context of an organization&#39;s activities. Certain embodiments of the invention reflect an appreciation that one advantage to such an approach is that information flowing through an organization, and the networks they employ, should be trackable, and substantial data breaches preventable. Skilled practitioners of the art will recognize that many such embodiments and examples are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
       FIGS. 6 a  and 6 b    show a simplified block diagram of an entity behavior profile (EBP) and a prepopulated EBP entity behavior profile implemented in accordance with an embodiment of the invention. As used herein, an entity behavior profile  638  broadly refers to a collection of information that uniquely describes a particular entity&#39;s identity and their associated behavior, whether the behavior occurs within a physical realm or cyberspace. In certain embodiments, an EBP  638  may be used to adaptively draw inferences regarding the trustworthiness of a particular entity. In certain embodiments, as described in greater detail herein, the drawing of the inferences may involve comparing a new entity behavior to known past behaviors enacted by the entity. In certain embodiments, new entity behavior of analytic utility may represent entity behavior that represents a security risk. As likewise used herein, an entity broadly refers to something that exists as itself, whether physically or abstractly. In certain embodiments, an entity may be a user entity, a non-user entity, or a combination thereof. In certain embodiments, the identity of an entity may be known or unknown. 
     As used herein, a user entity broadly refers to an entity capable of enacting a user entity behavior, as described in greater detail herein. Examples of a user entity include an individual person, a group of people, an organization, or a government. As likewise used herein, a non-user entity broadly refers to an entity whose identity can be described and may exhibit certain behavior, but is incapable of enacting a user entity behavior. Examples of a non-user entity include an item, a device, such as endpoint and edge devices, a network, an account, a domain, an operation, a process, and an event. Other examples of a non-user entity include a resource, such as a geographical location or formation, a physical facility, a venue, a system, a software application, a data store, and a service, such as a service operating in a cloud environment. 
     Certain embodiments of the invention reflect an appreciation that being able to uniquely identity a device may assist in establishing whether or not a particular login is legitimate. As an example, user impersonations may not occur at the user&#39;s endpoint, but instead, from another device or system. Certain embodiments of the invention likewise reflect an appreciation that profiling the entity behavior of a particular device or system may assist in determining whether or not it is acting suspiciously. 
     In certain embodiments, an account may be local account, which runs on a single machine. In certain embodiments, an account may be a global account, providing access to multiple resources. In certain embodiments, a process may be implemented to run in an unattended mode, such as when backing up files or checking for software updates. Certain embodiments of the invention reflect an appreciation that it is often advantageous to track events at the process level as a method of determining which events are associated with background processes and which are initiated by a user entity. 
     In certain embodiments, an EBP  638  may be implemented to include a user entity profile  602 , an associated user entity mindset profile  630 , a non-user entity profile  632 , and an entity state  636 . As used herein, a user entity profile  602  broadly refers to a collection of information that uniquely describes a user entity&#39;s identity and their associated behavior, whether the behavior occurs within a physical realm or cyberspace. In certain embodiments, as described in greater detail herein, the user entity profile  602  may include user profile attributes  604 , user behavior factors  610 , user mindset factors  622 , or a combination thereof. In certain embodiments, the user profile attributes  604  may include certain user authentication factors  606 , described in greater detail herein, and personal information  608 . 
     As used herein, a user profile attribute  604  broadly refers to data or metadata that can be used, individually or in combination with other user profile attributes  604 , user behavior factors  610 , or user mindset factors  622 , to ascertain the identity of a user entity. In various embodiments, certain user profile attributes  604  may be uniquely associated with a particular user entity. In certain embodiments, the personal information  608  may include non-sensitive personal information associated with a user entity, such as their name, title, position, role, and responsibilities. In certain embodiments, the personal information  608  may likewise include technical skill level information, peer information, expense account information, paid time off (PTO) information, data analysis information, insider information, misconfiguration information, third party information, or a combination thereof. In certain embodiments, the personal information  608  may contain sensitive personal information associated with a user entity. As used herein, sensitive personal information (SPI), also commonly referred to as personally identifiable information (PII), broadly refers to any information usable to ascertain the identity of a user entity, either by itself, or in combination with other information, such as contextual information described in greater detail herein. 
     Examples of SPI may include the full or legal name of a user entity, initials or nicknames, place and date of birth, home and business addresses, personal and business telephone numbers, their gender, and other genetic information. Additional examples of SPI may include government-issued identifiers, such as a Social Security Number (SSN) or a passport number, vehicle registration plate and serial numbers, and driver&#39;s license numbers. Other examples of SPI may include certain email addresses and social media identifiers, credit and debit card numbers, and other digital identity information. Yet other examples of SPI may include employer-issued identifiers, financial transaction information, credit scores, electronic medical records (EMRs), insurance claim information, personal correspondence, and so forth. Further examples of SPI may include user authentication factors  606 , such as biometrics, user identifiers and passwords, and personal identification numbers (PINs). 
     In certain embodiments, the SPI may include information considered by an individual user, a group of users, or an organization (e.g., a company, a government or non-government organization, etc.), to be confidential or proprietary. One example of such confidential information is protected health information (PHI). As used herein, PHI broadly refers to any information associated with the health status, provision of health care, or payment for health care that is created or collected by a “covered entity,” or an associate thereof, that can be linked to a particular individual. As used herein, a “covered entity” broadly refers to health plans, healthcare clearinghouses, healthcare providers, and others, who may electronically communicate any health-related information associated with a particular individual. Examples of such PHI may include any part of a patient&#39;s medical record, healthcare record, or payment history for medical or healthcare services. 
     As used herein, a user behavior factor  610  broadly refers to information associated with a user entity&#39;s behavior, whether the behavior occurs within a physical realm or cyberspace. In certain embodiments, user behavior factors  610  may include the user entity&#39;s access rights  612 , the user entity&#39;s interactions  614 , and the date/time/frequency  616  of when the interactions  614  are enacted. In certain embodiments, the user behavior factors  610  may likewise include the user entity&#39;s location  618 , and the gestures  620  used by the user entity to enact the interactions  614 . 
     In certain embodiments, the user entity gestures  620  may include key strokes on a keypad, a cursor movement, a mouse movement or click, a finger swipe, tap, or other hand gesture, an eye movement, or some combination thereof. In certain embodiments, the user entity gestures  620  may likewise include the cadence of the user&#39;s keystrokes, the motion, force and duration of a hand or finger gesture, the rapidity and direction of various eye movements, or some combination thereof. In certain embodiments, the user entity gestures  620  may include various audio or verbal commands performed by the user. 
     As used herein, user mindset factors  622  broadly refer to information used to make inferences regarding the mental state of a user entity at a particular point in time, during the occurrence of an event or an enactment of a user behavior, or a combination thereof. As likewise used herein, mental state broadly refers to a hypothetical state corresponding to the way a user entity may be thinking or feeling. Likewise, as used herein, an event broadly refers to the occurrence of an action performed by an entity. In certain embodiments, the user entity mindset factors  622  may include a personality type  624 . Examples of known approaches for determining a personality type  624  include Jungian types, Myers-Briggs type indicators, Keirsey Temperament Sorter, Socionics, Enneagram of Personality, and Eyseneck&#39;s three-factor model. 
     In certain embodiments, the user mindset factors  622  may include various behavioral biometrics  626 . As used herein, a behavioral biometric  628  broadly refers to a physiological indication of a user entity&#39;s mental state. Examples of behavioral biometrics  626  may include a user entity&#39;s blood pressure, heart rate, respiratory rate, eye movements and iris dilation, facial expressions, body language, tone and pitch of voice, speech patterns, and so forth. 
     Certain embodiments of the invention reflect an appreciation that certain user behavior factors  610 , such as user entity gestures  620 , may provide additional information related to inferring a user entity&#39;s mental state. As an example, a user entering text at a quick pace with a rhythmic cadence may indicate intense focus. Likewise, an individual user intermittently entering text with forceful keystrokes may indicate the user is in an agitated state. As another example, the user may intermittently enter text somewhat languorously, which may indicate being in a thoughtful or reflective state of mind. As yet another example, the user may enter text with a light touch with an uneven cadence, which may indicate the user is hesitant or unsure of what is being entered. 
     Certain embodiments of the invention likewise reflect an appreciation that while the user entity gestures  620  may provide certain indications of the mental state of a particular user entity, they may not provide the reason for the user entity to be in a particular mental state. Likewise, certain embodiments of the invention include an appreciation that certain user entity gestures  620  and behavioral biometrics  626  are reflective of an individual user&#39;s personality type  624 . As an example, aggressive, forceful keystrokes combined with an increased heart rate may indicate normal behavior for a particular user when composing end-of-month performance reviews. In various embodiments, certain user entity behavior factors  610 , such as user gestures  620 , may be correlated with certain contextual information, as described in greater detail herein. 
     In certain embodiments, a security analytics system  118 , described in greater detail herein, may be implemented to include an entity behavior catalog (EBC) system  120 . In certain embodiments, the EBC system  120  may be implemented to generate, manage, store, or some combination thereof, information related to the behavior of an associated entity. In various embodiments, the EBC system  120  may be implemented as a cyber behavior catalog. In certain of these embodiments, the cyber behavior catalog may be implemented to generate, manage, store, or some combination thereof, information related to cyber behavior, described in greater detail herein, enacted by an associated entity. In various embodiments, as likewise described in greater detail herein, the information generated, managed, stored, or some combination thereof, by such a cyber behavior catalog, may be related to cyber behavior enacted by a user entity, a non-user entity, or a combination thereof. 
     In certain embodiments, the EBC system  120  may be implemented to use a user entity profile  602  in combination with an entity state  636  to generate a user entity mindset profile  630 . As used herein, entity state  636  broadly refers to the context of a particular event or entity behavior. In certain embodiments, the entity state  636  may be a long-term entity state or a short-term entity state. As used herein, a long-term entity state  636  broadly relates to an entity state  636  that persists for an extended interval of time, such as six months or a year. As likewise used herein, a short-term entity state  636  broadly relates to an entity state  636  that occurs for a brief interval of time, such as a few minutes or a day. In various embodiments, the method by which an entity state&#39;s  636  associated interval of time is considered to be long-term or short-term is a matter of design choice. 
     As an example, a particular user may have a primary work location, such as a branch office, and a secondary work location, such as their company&#39;s corporate office. In this example, the user&#39;s primary and secondary offices respectively correspond to the user&#39;s location  618 , whereas the presence of the user at either office corresponds to an entity state  636 . To continue the example, the user may consistently work at their primary office Monday through Thursday, but at their company&#39;s corporate office on Fridays. To further continue the example, the user&#39;s presence at their primary work location may be a long-term entity state  636 , while their presence at their secondary work location may be a short-term entity state  636 . Accordingly, a date/time/frequency  616  user entity behavior factor  614610  can likewise be associated with user behavior respectively enacted on those days, regardless of their corresponding locations. Consequently, the long-term user entity state  636  on Monday through Thursday will typically be “working at the branch office” and the short-term entity state  636  on Friday will likely be “working at the corporate office.” 
     As likewise used herein, a user entity mindset profile  630  broadly refers to a collection of information that reflects an inferred mental state of a user entity at a particular time during the occurrence of an event or an enactment of a user behavior. As an example, certain information may be known about a user entity, such as their name, their title and position, and so forth, all of which are user profile attributes  604 . Likewise, it may be possible to observe a user entity&#39;s associated user behavior factors  610 , such as their interactions with various systems, when they log-in and log-out, when they are active at the keyboard, the rhythm of their keystrokes, and which files they typically use. 
     Certain embodiments of the invention reflect an appreciation these behavior factors  610  can be considered to be a behavioral fingerprint. In certain embodiments, the user behavior factors  610  may change, a little or a lot, from day to day. These changes may be benign, such as when a user entity begins a new project and accesses new data, or they may indicate something more concerning, such as a user entity who is actively preparing to steal data from their employer. In certain embodiments, the user behavior factors  610  may be implemented to ascertain the identity of a user entity. In certain embodiments, the user behavior factors  610  may be uniquely associated with a particular entity. 
     In certain embodiments, observed user behaviors may be used to build a user entity profile  602  for a particular user or other entity. In addition to creating a model of a user&#39;s various attributes and observed behaviors, these observations can likewise be used to infer things that are not necessarily explicit. Accordingly, in certain embodiments, a behavioral fingerprint may be used in combination with an EBP  638  to generate an inference regarding an associated user entity. As an example, a particular user may be observed eating a meal, which may or may not indicate the user is hungry. However, if it is also known that the user worked at their desk throughout lunchtime and is now eating a snack during a mid-afternoon break, then it can be inferred they are indeed hungry. 
     As likewise used herein, a non-user entity profile  632  broadly refers to a collection of information that uniquely describes a non-user entity&#39;s identity and their associated behavior, whether the behavior occurs within a physical realm or cyberspace. In various embodiments, the non-user entity profile  632  may be implemented to include certain non-user profile attributes  634 . As used herein, a non-user profile attribute  634  broadly refers to data or metadata that can be used, individually or in combination with other non-user profile attributes  634 , to ascertain the identity of a non-user entity. In various embodiments, certain non-user profile attributes  634  may be uniquely associated with a particular non-user entity. 
     In certain embodiments, the non-user profile attributes  634  may be implemented to include certain identity information, such as a non-user entity&#39;s network, Media Access Control (MAC), or physical address, its serial number, associated configuration information, and so forth. In various embodiments, the non-user profile attributes  634  may be implemented to include non-user behavior information associated with interactions between certain user and non-user entities, the type of those interactions, the data exchanged during the interactions, the date/time/frequency of such interactions, and certain services accessed or provided. 
     In certain embodiments, the EBC system  120  may be implemented to include an EBC access management  122 , an EBP management  124 , a security vulnerability scenario management  126 , a security risk use case management  128 , an event enrichment  680 , an analytic utility detection  682 , an entity behavior contextualization  684 , an entity behavior meaning derivation  686 , and an entity data anonymization  688  module or a combination thereof. In various embodiments, the EBC access management  122  module may be implemented to provide access to certain functionalities performed by the EBC system  120 , as described in greater detail herein. In various embodiments, the EBP management  124  module may be implemented to perform certain EBP management operations, described in greater detail herein. In various embodiments, the security vulnerability scenario management  126  module may be implemented to perform certain security vulnerability scenario management operations, described in greater detail herein. 
     In various embodiments, the event enrichment  680  module may be implemented to perform certain event enrichment operations, described in greater detail herein. In various embodiments, the analytic utility detection  682  module may be implemented to perform certain analytic utility detection operations, as described in greater detail herein. In various embodiments, as described in greater detail herein, the entity behavior contextualization  684  module may be implemented to perform certain contextualization operations, as described in greater detail herein. As likewise described in greater detail herein, the entity behavior meaning derivation  686  module may be implemented in various embodiments to perform certain behavior meaning derivation operations. In certain embodiments, the entity data anonymization  688  module may be implemented various embodiments to perform certain data anonymization operations. In various embodiments, the EBC access management  122 , EBP management  124 , security vulnerability scenario management  126 , security risk use case management  128 , event enrichment  680 , analytic utility detection  682 , entity behavior contextualization  684 , entity behavior meaning derivation  686 , and entity data anonymization  688  modules, or a combination thereof, may be implemented to provide an EBC reference architecture for performing certain EBC operations, described in greater detail herein. 
     In various embodiments, as described in greater detail herein, the EBP system  120  may be implemented to use certain data associated with an EBP  638  to derive an inference for contextualizing an electronically-observable behavior of a corresponding entity. In certain embodiments, the EBP system  120  may be implemented to use a user entity profile  602  in combination with a user entity mindset profile  632  and an associated entity state  636  to infer a user entity&#39;s intent. In certain embodiments, the EBP system  120  may be implemented to use various data stored in a repository of EBP data  690  to perform such an inference. In certain embodiments, the repository of EBP data  690  may include various EBPs  638 , prepopulated EBPs  678 , and associated contextual information, described in greater detail herein. 
     In various embodiments, the EBC system  120  may be implemented to use certain data associated with an EBP  638  to provide a probabilistic measure of whether a particular electronically-observable event is of analytic utility. In certain embodiments, an electronically-observable event that is of analytic utility may be determined to be anomalous, abnormal, unexpected, or malicious. To continue the prior example, a user may typically work out of their company&#39;s corporate office on Fridays. Furthermore, various user mindset factors  622  within their associated user entity profile  602  may indicate that the user is typically relaxed and methodical when working with customer data. Moreover, the user&#39;s user entity profile  602  indicates that such user interactions  614  with customer data typically occur on Monday mornings and the user rarely, if ever, copies or downloads customer data. However, the user may decide to interact with certain customer data late at night, on a Friday, while in their company&#39;s corporate office. As they do so, they exhibit an increased heart rate, rapid breathing, and furtive keystrokes while downloading a subset of customer data to a flash drive. 
     Consequently, their user entity mindset profile  630  may reflect a nervous, fearful, or guilty mindset, which is inconsistent with the entity state  634  of dealing with customer data in general. More particularly, downloading customer data late at night on a day the user is generally not in their primary office results in an entity state  634  that is likewise inconsistent with the user&#39;s typical user behavior. As a result, the EBC system  120  may infer that the user&#39;s behavior may represent a security threat. Those of skill in the art will recognize that many such embodiments and examples are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
     Certain embodiments of the invention reflect an appreciation that the quantity, and relevancy, of information contained in a particular EBP  638  may have a direct bearing on its analytic utility when attempting to determine the trustworthiness of an associated entity and whether or not they represent a security risk. As used herein, the quantity of information contained in a particular EBP  638  broadly refers to the variety and volume of EBP elements it may contain, and the frequency of their respective instances, or occurrences, related to certain aspects of an associated entity&#39;s identity and behavior. As used herein, an EBP element broadly refers to any data element stored in an EBP  638 , as described in greater detail herein. In various embodiments, an EBP element may be used to describe a particular aspect of an EBP, such as certain user profile attributes  604 , user behavior factors  610 , user mindset factors  622 , user entity mindset profile  630 , non-user profile attributes  634 , and entity state  636 . 
     In certain embodiments, statistical analysis may be performed on the information contained in a particular EBP  638  to determine the trustworthiness of its associated entity and whether or not they represent a security risk. For example, a particular authentication factor  606 , such as a biometric, may be consistently used by a user entity for authenticating their identity to their endpoint device. To continue the example, a user ID and password may be used by the same, or a different user entity, in an attempt to access the endpoint device. As a result, the use of a user ID and password may indicate a security risk due to its statistical infrequency. As another example, a user entity may consistently access three different systems on a daily basis in their role as a procurement agent. In this example, the three systems may include a financial accounting system, a procurement system, and an inventory control system. To continue the example, an attempt by the procurement agent to access a sales forecast system may appear suspicious if never attempted before, even if the purpose for accessing the system is legitimate. 
     As likewise used herein, the relevancy of information contained in a particular EBP  638  broadly refers to the pertinence of the EBP elements it may contain to certain aspects of an associated entity&#39;s identity and behavior. To continue the prior example, an EBP  638  associated with the procurement agent may contain certain user profile attributes  604  related to their title, position, role, and responsibilities, all or which may be pertinent to whether or not they have a legitimate need to access the sales forecast system. In certain embodiments, the user profile attributes  604  may be implemented to include certain job description information. To further continue the example, such job description information may have relevance when attempting to determine whether or not the associated entity&#39;s behavior is suspicious. In further continuance of the example, job description information related to the procurement agent may include their responsibility to check sales forecast data, as needed, to ascertain whether or not to procure certain items. In these embodiments, the method by which it is determined whether the information contained in a particular EBP  638  is of sufficient quantity and relevancy is a matter of design choice. 
     Various embodiments of the invention likewise reflect an appreciation that accumulating sufficient information in an EBP  638  to make such a determination may take a certain amount of time. Likewise, various embodiments of the invention reflect an appreciation that the effectiveness or accuracy of such a determination may rely upon certain entity behaviors occurring with sufficient frequency, or in identifiable patterns, or a combination thereof, during a particular period of time. As an example, there may not be sufficient occurrences of a particular type of entity behavior to determine if a new entity behavior is inconsistent with known past occurrences of the same type of entity behavior. 
     Various embodiments of the invention reflect an appreciation that a sparsely-populated EBP  638  may likewise result in exposure to certain security vulnerabilities. Various embodiments of the invention likewise reflect an appreciation that an EBP  638  may be particularly sparsely populated when first implemented. Furthermore, the relevance of such sparsely-populated information initially contained in an EBP  638  first implemented may not prove very useful when using an EBP  638  to determine the trustworthiness of an associated entity and whether or not they represent a security risk. Accordingly, certain embodiments reflect an appreciation that the implementation of a prepopulated EBP  678  may provide a sufficient quantity of relevant information to improve the analytic utility of an EBP  638  when initially implemented. As used herein, a prepopulated EBP  678  broadly refers to a collection of information that generically describes a particular entity&#39;s expected behavior, whether the behavior occurs within a physical realm or cyberspace. 
     In certain embodiments, an entity&#39;s expected behavior may be determined by using one or more existing EBPs  638  associated with similarly situated entities as a reference when generating a prepopulated EBP  678 . As used herein, similarly situated entities broadly refer to entities whose associated EBP  638  contain one or more EBP elements sharing substantively similar entity characteristics associated with a target entity. As likewise used herein, entity characteristics broadly refer to characteristics that can be used to distinguish certain attributes associated with a particular entity. Likewise, substantively similar entity characteristics, as used herein, broadly refer to at least one equivalent entity characteristic, such as the same job title, same job description, same role, one or more of the same duties or responsibilities, and so forth. 
     In various embodiments, certain personal information  608 , described in greater detail herein, may be anonymized, as likewise described in greater detail herein, and used as an entity characteristic. Examples of such anonymized entity characteristics may include name, gender, geographic location, citizenship, country of origin, and so forth. In certain embodiments, certain user mindset factors  622 , such as an entity&#39;s personality type  624 , may likewise be anonymized and used as an entity characteristic. In certain embodiments, EBPs  638  respectively associated with a collection of distinct entities may be processed to determine their respective entity characteristics. In certain embodiments, the resulting entity characteristics may be used to segment the collection of distinct entities into one or more groups of similarly situated entities. 
     In various embodiments, a particular entity characteristic may correspond to a user profile attribute, a user behavior factor, or a user mindset factor contained in an EBP  638  associated with one or more similarly situated entities. As an example, an organization may employ five financial analysts, each of which has an associated EBP  638  containing information related to their observed behavior. In this example, the information respectively related to the observed behavior of the financial analysts may be aggregated and normalized to determine, in general, the expected behavior of a financial analyst. 
     To continue the example, the resulting information related to the expected behavior of a financial analyst can then be used as baseline behavior information for populating a prepopulated EBP  678 , which in turn can be associated with a newly-hired financial analyst as their EBP  638 . It will be appreciated that the implementation of such baseline behavioral information in certain embodiments may provide a basis for comparing an entity&#39;s expected behavior to their observed behavior, and as a result, assist in the identification of suspicious behavior. 
     As another example, a security analytics system  118  may be implemented to provide various security services, described in greater detail herein, for a large public school system. In this example, one of the employees of the school system is their head dietician. One entity characteristic of the head dietician is they are a senior administrator in a public school system. Another entity characteristic is they are responsible for defining cost-effective, nutritional meals for students. Yet another entity characteristic is they are responsible for managing a multi-million dollar budget. Yet still another entity characteristic is they manage a staff numbering in the hundreds. An additional entity characteristic is they are authorized to access the school districts enterprise resource planning (ERP) system and make adjustments to budget projections. 
     In this example, the first entity characteristic may be used to identify a group of similarly situated entities whose associated EBP  638  signify they are an administrator in a public school system. Likewise, the second entity characteristic may be used to further refine the group of similarly situated entities to identify those entities whose associated EBP  638  signify they have the role of dietician in a school system, with associated meal planning responsibilities. In turn, the third entity characteristic may likewise be used to yet further refine the group of similarly situated entities to identify those entities whose associated EBP  638  signify they have a yearly budget responsibility exceeding one million dollars. 
     Additionally, the fourth entity characteristic may be used to yet still further refine the group of similarly situated entities to identify those entities whose associated EBP  638  signify they manage at least one hundred staff members. Finally, the fifth entity characteristic may be used to yet still further refine the group of similarly situated entities to identify those entities whose associated EBP  638  signify they have the right to access systems related to making revisions to their budget projections. To continue the example, the EBPs  638  associated with the resulting group of similarly situated entities may then be used as the basis to generate a prepopulated EBP  678  for the head dietician that matches their associated entity characteristics. 
     Certain embodiments of the invention reflect an appreciation that it may not always be possible to identify a similarly situated entity who&#39;s associated EBP  638  signify they have the same entity characteristics as a target entity. Accordingly, the EBPs  638  associated with two or more similarly situated entities may be used in certain embodiments to generate a prepopulated EBP  678  when their respective EBPs  638  signify they have at least one of the same entity characteristics as the target entity. 
     To continue the preceding example, a first similarly situated entity may have an associated EBP  638  signifying they are an administrator in a public school system, they have the role of dietician in a school system, with associated meal planning responsibilities, and they have a yearly budget responsibility exceeding one million dollars. Likewise, a second similarly situated entity may have an associated EBP  638  signifying they are an administrator in a public school system, they manage at least one hundred staff members, and they have the right to access systems related to making revisions to their budget projections. In continuance of this example the EBPs  638  respectively associated with the first and second similarly situated entities may be processed to generate a prepopulated EBP  678  for the head dietician that matches their associated entity characteristics. 
     Certain embodiments of the invention reflect an appreciation that the two or more similarly situated entities whose respective EBPs  638  are used to generate a prepopulated EBP  678  may or may not be associated. In further continuance of the preceding example, the first and second similarly situated entities may be associated with the same school system. Conversely, the first and second similarly situated entities may be associated with different school systems. Skilled practitioners of the art will recognize that many such embodiments and examples are possible. Accordingly, the foregoing is not intended to limit the spirit, scope, or intent of the invention. 
     In various embodiments, a prepopulated EBP  678  may be implemented to include certain parameters describing an entity&#39;s expected behavior. In various embodiments, certain entity characteristic information, such as job titles, descriptions, roles, duties, responsibilities, and so forth, may be used to define such parameters in a prepopulated EBP  678 . In certain embodiments, such entity attribute information may be stored in a repository of entity attribute data  690 . In various embodiments, a prepopulated EBP  678  may be implemented as an EBP  638  template. In certain of these embodiments, the EBP  638  template defines which information related to an entity&#39;s identity and behavioral will be collected by the EBP  638 . 
     In certain embodiments, the information contained in, or referenced by, a prepopulated EBP  678  may be normalized across multiple entities. In various embodiments, certain personally-identifiable information (PII), described in greater detail herein, associated with such entities may be anonymized before its inclusion in a prepopulated EBP  678 . In certain embodiments, the anonymization of such PII information may be performed by an entity data anonymization  688  module. 
     In certain embodiments, a prepopulated EBP  678  may be implemented to mirror the structure of a corresponding EBP  638 . For example, as shown in  FIG. 6 a   , an EBP  638  may be implemented to contain a user entity profile  602 , a user entity mindset profile  630 , a non-user entity profile  632 , and an entity state  636 . As likewise shown in  FIG. 6 b   , a corresponding prepopulated EBP  678  may be implemented to contain a prepopulated user entity profile  642 , a prepopulated user entity mindset profile  670 , a prepopulated non-user entity profile  672 , and a prepopulated entity state  676 . 
     As shown in  FIG. 6 a   , the user entity profile  602  of the EBP  638  may include certain user profile attributes  604 , user behavior factors  610  and user mindset factors  622 . Likewise, as shown in  FIG. 6 a   , the user profile attributes  604  may include certain EBP elements related to authentication factors  606  and personal information  608 . As likewise shown in  FIG. 6 a   , the user behavior factors  610  may include certain EBP elements related to user access rights  612 , user interactions  614 , date/time/frequency  616 , user location  618 , and user gestures  620 . Likewise, the user mindset factors  622  shown in  FIG. 6 a    may include certain EBP elements related to personality type  624  and behavioral biometrics  626 , while the non-user entity profile  632  may include certain EBP elements related to non-user profile attributes  634 . 
     Likewise, as shown in  FIG. 6 b   , the corresponding prepopulated EBP  678  may include certain prepopulated user profile attributes  644 , prepopulated user behavior factors  650 , and prepopulated user mindset factors  662 . Likewise, as shown in  FIG. 6 b   , the prepopulated user profile attributes  604  may include certain prepopulated EBP elements related to prepopulated authentication factors  646  and prepopulated personal information  648 . As likewise shown in  FIG. 6 b   , the corresponding prepopulated user behavior factors  610  may include certain prepopulated EBP elements related to prepopulated user access rights  652 , prepopulated user interactions  654 , prepopulated date/time/frequency  656 , prepopulated user location  658 , and prepopulated user gestures  660 . Likewise, the prepopulated user mindset factors  622  shown in  FIG. 6 b    may include certain prepopulated EBP elements related to prepopulated personality type  664  and prepopulated behavioral biometrics  668 , while the prepopulated non-user entity profile  672  may include certain prepopulated EBP elements related to prepopulated non-user profile attributes  674 . 
     As used herein, a prepopulated EBP element broadly refers to any data element stored in a prepopulated EBP  678 . In certain embodiments, an EBP element stored in an EBP  638  associated with a particular entity may be used as a prepopulated EBP element in a corresponding prepopulated EBP  678 . In certain embodiments one or more EBP elements respectively stored in one or more associate EBPs  638  may be used, individually or in combination, as prepopulated EBP elements in a prepopulated EBP  678 . In certain embodiments, the entity data anonymization  688  module may be used to perform anonymization operations to anonymize certain EBP elements prior to being used as prepopulated EBP elements in a prepopulated EBP  678 . Skilled practitioners of the art will recognize that many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope, or intent of the invention. 
       FIGS. 7 a  and 7 b    show a block diagram of a security analytics environment implemented in accordance with an embodiment of the invention. In certain embodiments, a security analytics system  118  may be implemented with an entity behavior catalog (EBC) system  120 , described in greater detail herein. In certain embodiments, analyses performed by the security analytics system  118  may be used to identify behavior associated with a particular entity that may be of analytic utility. In certain embodiments, as likewise described in greater detail herein, the EBC system  120  may be used in combination with the security analytics system  120  to perform such analyses. In various embodiments, certain data stored in a repository of security analytics data, or a repository of EBC data  690 , or both, may be used by the security analytics system  118 , or the EBC system  120 , or both, to perform the analyses. 
     In certain embodiments, the entity behavior of analytic utility may be identified at a particular point in time, during the occurrence of an event, the enactment of a user or non-user entity behavior, or a combination thereof. As used herein, an entity broadly refers to something that exists as itself, whether physically or abstractly. In certain embodiments, an entity may be a user entity, a non-user entity, or a combination thereof. In certain embodiments, a user entity may be an individual user, such as user ‘A’  702  or ‘B’  772 , a group, an organization, or a government. In certain embodiments, a non-user entity may likewise be an item, a device, such as endpoint  304  and edge  202  devices, a network, such as an internal  744  and external  746  networks, a domain, an operation, or a process. In certain embodiments, a non-user entity may be a resource  750 , such as a geographical location or formation, a physical facility  752 , such as a venue, various physical security devices  754 , a system  756 , shared devices  758 , such as printer, scanner, or copier, a data store  760 , or a service  762 , such as a service  762  operating in a cloud environment. 
     As likewise used herein, an event broadly refers to the occurrence of an action performed by an entity. In certain embodiments, the action may be directly associated with an entity behavior, described in greater detail herein. As an example, a first user may attach a binary file infected with a virus to an email that is subsequently sent to a second user. In this example, the act of attaching the binary file to the email is directly associated with an entity behavior enacted by the first user. In certain embodiments, the action may be indirectly associated with an entity behavior. To continue the example, the recipient of the email may open the infected binary file, and as a result, infect their computer with malware. To further continue the example, the act of opening the infected binary file is directly associated with an entity behavior enacted by the second user. However, the infection of the email recipient&#39;s computer by the infected binary file is indirectly associated with the described entity behavior enacted by the second user. 
     In various embodiments, certain user authentication factors  606  may be used to authenticate the identity of a user entity. In certain embodiments, the user authentication factors  606  may be used to ensure that a particular user entity, such as user ‘A’  702  or ‘B’  772 , is associated with their corresponding user entity profile  602 , rather than a user entity profile  602  associated with another user. In certain embodiments, the user authentication factors  606  may include a user&#39;s biometrics  706  (e.g., a fingerprint or retinal scan), tokens  708  (e.g., a dongle containing cryptographic keys), user identifiers and passwords (ID/PW)  710 , and personal identification numbers (PINs). 
     In certain embodiments, information associated with such user entity behavior may be stored in a user entity profile  602 , described in greater detail herein. In certain embodiments, the user entity profile  602  may be stored in a repository of entity behavior catalog (EBC) data  690 . In certain embodiments, as likewise described in greater detail herein, the user entity profile  602  may include user profile attributes  604 , user behavior factors  610 , user mindset factors  622 , or a combination thereof. As used herein, a user profile attribute  604  broadly refers to data or metadata that can be used, individually or in combination with other user profile attributes  604 , user behavior factors  610 , or user mindset factors  622 , to ascertain the identity of a user entity. In various embodiments, certain user profile attributes  604  may be uniquely associated with a particular user entity. 
     As likewise used herein, a user behavior factor  610  broadly refers to information associated with a user&#39;s behavior, whether the behavior occurs within a physical realm or cyberspace. In certain embodiments, the user behavior factors  610  may include the user&#39;s access rights  612 , the user&#39;s interactions  614 , and the date/time/frequency  616  of those interactions  614 . In certain embodiments, the user behavior factors  610  may likewise include the user&#39;s location  618  when the interactions  614  are enacted, and the user gestures  620  used to enact the interactions  614 . 
     In various embodiments, certain date/time/frequency  616  user behavior factors  610  may be implemented as ontological or societal time, or a combination thereof. As used herein, ontological time broadly refers to how one instant in time relates to another in a chronological sense. As an example, a first user behavior enacted at 12:00 noon on May 17, 2017 may occur prior to a second user behavior enacted at 6:39 PM on May 18, 2018. Skilled practitioners of the art will recognize one value of ontological time is to determine the order in which various user behaviors have been enacted. 
     As likewise used herein, societal time broadly refers to the correlation of certain user profile attributes  604 , user behavior factors  610 , user mindset factors  622 , or a combination thereof, to one or more instants in time. As an example, user ‘A’  702  may access a particular system  756  to download a customer list at 3:47 PM on Nov. 3, 2017. Analysis of their user behavior profile indicates that it is not unusual for user ‘A’  702  to download the customer list on a weekly basis. However, examination of their user behavior profile also indicates that user ‘A’  702  forwarded the downloaded customer list in an email message to user ‘B’  772  at 3:49 PM that same day. Furthermore, there is no record in their user behavior profile that user ‘A’  702  has ever communicated with user ‘B’  772  in the past. Moreover, it may be determined that user ‘B’  872  is employed by a competitor. Accordingly, the correlation of user ‘A’  702  downloading the customer list at one point in time, and then forwarding the customer list to user ‘B’  772  at a second point in time shortly thereafter, is an example of societal time. 
     In a variation of the prior example, user ‘A’  702  may download the customer list at 3:47 PM on Nov. 3, 2017. However, instead of immediately forwarding the customer list to user ‘B’  772 , user ‘A’  702  leaves for a two week vacation. Upon their return, they forward the previously-downloaded customer list to user ‘B’  772  at 9:14 AM on Nov. 20, 2017. From an ontological time perspective, it has been two weeks since user ‘A’  702  accessed the system  756  to download the customer list. However, from a societal time perspective, they have still forwarded the customer list to user ‘B’  772 , despite two weeks having elapsed since the customer list was originally downloaded. 
     Accordingly, the correlation of user ‘A’  702  downloading the customer list at one point in time, and then forwarding the customer list to user ‘B’  772  at a much later point in time, is another example of societal time. More particularly, it may be inferred that the intent of user ‘A’  702  did not change during the two weeks they were on vacation. Furthermore, user ‘A’  702  may have attempted to mask an intended malicious act by letting some period of time elapse between the time they originally downloaded the customer list and when they eventually forwarded it to user ‘B’  772 . From the foregoing, those of skill in the art will recognize that the use of societal time may be advantageous in determining whether a particular entity behavior is of analytic utility. As used herein, mindset factors  622  broadly refer to information used to infer the mental state of a user at a particular point in time, during the occurrence of an event, an enactment of a user behavior, or combination thereof. 
     In certain embodiments, the security analytics system  118  may be implemented to process certain entity attribute information, described in greater detail herein, associated with providing resolution of the identity of an entity at a particular point in time. In various embodiments, the security analytics system  118  may be implemented to use certain entity identifier information, likewise described in greater detail herein, to ascertain the identity of an associated entity at a particular point in time. In various embodiments, the entity identifier information may include certain temporal information, described in greater detail herein. In certain embodiments, the temporal information may be associated with an event associated with a particular point in time. 
     In certain embodiments, the security analytics system  118  may be implemented to use information associated with certain entity behavior elements to resolve the identity of an entity at a particular point in time. An entity behavior element, as used herein, broadly refers to a discrete element of an entity&#39;s behavior during the performance of a particular operation in a physical realm, cyberspace, or a combination thereof. In certain embodiments, such entity behavior elements may be associated with a user/device  730 , a user/network  742 , a user/resource  748 , a user/user  770  interaction, or a combination thereof. 
     As an example, user ‘A’  702  may use an endpoint device  304  to browse a particular web page on a news site on an external system  776 . In this example, the individual actions performed by user ‘A’  702  to access the web page are entity behavior elements that constitute an entity behavior, described in greater detail herein. As another example, user ‘A’  702  may use an endpoint device  304  to download a data file from a particular system  756 . In this example, the individual actions performed by user ‘A’  702  to download the data file, including the use of one or more user authentication factors  606  for user authentication, are entity behavior elements that constitute an entity behavior. In certain embodiments, the user/device  730  interactions may include an interaction between a user, such as user ‘A’  702  or ‘B’  772 , and an endpoint device  304 . 
     In certain embodiments, the user/device  730  interaction may include interaction with an endpoint device  304  that is not connected to a network at the time the interaction occurs. As an example, user ‘A’  702  or ‘B’  772  may interact with an endpoint device  304  that is offline, using applications  732 , accessing data  734 , or a combination thereof, it may contain. Those user/device  730  interactions, or their result, may be stored on the endpoint device  304  and then be accessed or retrieved at a later time once the endpoint device  304  is connected to the internal  744  or external  746  networks. In certain embodiments, an endpoint agent  306  may be implemented to store the user/device  730  interactions when the user device  304  is offline. 
     In certain embodiments, an endpoint device  304  may be implemented with a device camera  728 . In certain embodiments, the device camera  728  may be integrated into the endpoint device  304 . In certain embodiments, the device camera  728  may be implemented as a separate device configured to interoperate with the endpoint device  304 . As an example, a webcam familiar to those of skill in the art may be implemented receive and communicate various image and audio signals to an endpoint device  304  via a Universal Serial Bus (USB) interface. 
     In certain embodiments, the device camera  728  may be implemented to capture and provide user/device  730  interaction information to an endpoint agent  306 . In various embodiments, the device camera  728  may be implemented to provide surveillance information related to certain user/device  730  or user/user  770  interactions. In certain embodiments, the surveillance information may be used by the security analytics system  118  to detect entity behavior associated with a user entity, such as user ‘A’  702  or user ‘B’  772  that may be of analytic utility. 
     In certain embodiments, the endpoint device  304  may be used to communicate data through the use of an internal network  744 , an external network  746 , or a combination thereof. In certain embodiments, the internal  744  and the external  746  networks may include a public network, such as the Internet, a physical private network, a virtual private network (VPN), or any combination thereof. In certain embodiments, the internal  744  and external  746  networks may likewise include a wireless network, including a personal area network (PAN), based on technologies such as Bluetooth. In various embodiments, the wireless network may include a wireless local area network (WLAN), based on variations of the IEEE 802.11 specification, commonly referred to as WiFi. In certain embodiments, the wireless network may include a wireless wide area network (WWAN) based on an industry standard including various 3G, 4G and 5G technologies. 
     In certain embodiments, the user/user  770  interactions may include interactions between two or more user entities, such as user ‘A’  702  and ‘B’  772 . In certain embodiments, the user/user interactions  770  may be physical, such as a face-to-face meeting, via a user/device  730  interaction, a user/network  742  interaction, a user/resource  748  interaction, or some combination thereof. In certain embodiments, the user/user  770  interaction may include a face-to-face verbal exchange. In certain embodiments, the user/user  770  interaction may include a written exchange, such as text written on a sheet of paper. In certain embodiments, the user/user  770  interaction may include a face-to-face exchange of gestures, such as a sign language exchange. 
     In certain embodiments, temporal event information associated with various user/device  730 , user/network  742 , user/resource  748 , or user/user  770  interactions may be collected and used to provide real-time resolution of the identity of an entity at a particular point in time. Those of skill in the art will recognize that many such examples of user/device  730 , user/network  742 , user/resource  748 , and user/user  770  interactions are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
     In various embodiments, the security analytics system  118  may be implemented to process certain contextual information in the performance of certain security analytic operations. As used herein, contextual information broadly refers to any information, directly or indirectly, individually or in combination, related to a particular entity behavior. In certain embodiments, entity behavior may include a user entity&#39;s physical behavior, cyber behavior, or a combination thereof. As likewise used herein, a user entity&#39;s physical behavior broadly refers to any user behavior occurring within a physical realm, such as speaking, gesturing, facial patterns or expressions, walking, and so forth. More particularly, such physical behavior may include any action enacted by an entity user that can be objectively observed, or indirectly inferred, within a physical realm. In certain embodiments, the objective observation, or indirect inference, of the physical behavior may be performed electronically. 
     As an example, a user may attempt to use an electronic access card to enter a secured building at a certain time. In this example, the use of the access card to enter the building is the action and the reading of the access card makes the user&#39;s physical behavior electronically-observable. As another example, a first user may physically transfer a document to a second user, which is captured by a video surveillance system. In this example, the physical transferal of the document from the first user to the second user is the action. Likewise, the video record of the transferal makes the first and second user&#39;s physical behavior electronically-observable. As used herein, electronically-observable user behavior broadly refers to any behavior exhibited or enacted by a user entity that can be observed through the use of an electronic device (e.g., an electronic sensor), a computing device or system (e.g., an endpoint  304  or edge  202  device, a physical security device  754 , a system  756 , a shared device  758 , etc.), computer instructions (e.g., a software application), or a combination thereof. 
     Cyber behavior, as used herein, broadly refers to any behavior occurring in cyberspace, whether enacted by an individual user, a group of users, or a system acting at the behest of an individual user, a group of users, or other entity. More particularly, cyber behavior may include physical, social, or mental actions that can be objectively observed, or indirectly inferred, within cyberspace. As an example, a user may use an endpoint device  304  to access and browse a particular website on the Internet. In this example, the individual actions performed by the user to access and browse the website constitute a cyber behavior. As another example, a user may use an endpoint device  304  to download a data file from a particular system  756  at a particular point in time. In this example, the individual actions performed by the user to download the data file, and associated temporal information, such as a time-stamp associated with the download, constitute a cyber behavior. In these examples, the actions are enacted within cyberspace, in combination with associated temporal information, which makes them electronically-observable. 
     In certain embodiments, the contextual information may include location data  736 . In certain embodiments, the endpoint device  304  may be configured to receive such location data  736 , which is used as a data source for determining the user&#39;s location  618 . In certain embodiments, the location data  736  may include Global Positioning System (GPS) data provided by a GPS satellite  738 . In certain embodiments, the location data  736  may include location data  736  provided by a wireless network, such as from a cellular network tower  740 . In certain embodiments (not shown), the location data  736  may include various Internet Protocol (IP) or other network address information assigned to the endpoint  304  or edge  202  device. In certain embodiments (also not shown), the location data  736  may include recognizable structures or physical addresses within a digital image or video recording. 
     In certain embodiments, the endpoint devices  304  may include an input device (not shown), such as a keypad, magnetic card reader, token interface, biometric sensor, and so forth. In certain embodiments, such endpoint devices  304  may be directly, or indirectly, connected to a particular facility  752 , physical security device  754 , system  756 , or shared device  758 . As an example, the endpoint device  304  may be directly connected to an ingress/egress system, such as an electronic lock on a door or an access gate of a parking garage. As another example, the endpoint device  304  may be indirectly connected to a physical security device  754  through a dedicated security network. 
     In certain embodiments, the security analytics system  118  may be implemented to perform various risk-adaptive protection operations. Risk-adaptive, as used herein, broadly refers to adaptively responding to risks associated with an electronically-observable entity behavior. In various embodiments, the security analytics system  118  may be implemented to perform certain risk-adaptive protection operations by monitoring certain entity behaviors, assess the corresponding risk they may represent, individually or in combination, and respond with an associated response. In certain embodiments, such responses may be based upon contextual information, described in greater detail herein, associated with a given entity behavior. 
     In certain embodiments, various information associated with a user entity profile  602 , likewise described in greater detail herein, may be used to perform the risk-adaptive protection operations. In certain embodiments, the user entity profile  602  may include user profile attributes  604 , user behavior factors  610 , user mindset factors  622 , or a combination thereof. In these embodiments, the information associated with a user entity profile  602  used to perform the risk-adaptive protection operations is a matter of design choice. 
     In certain embodiments, the security analytics system  118  may be implemented as a stand-alone system. In certain embodiments, the security analytics system  118  may be implemented as a distributed system. In certain embodiment, the security analytics system  118  may be implemented as a virtual system, such as an instantiation of one or more virtual machines (VMs). In certain embodiments, the security analytics system  118  may be implemented as a security analytics service  764 . In certain embodiments, the security analytics service  764  may be implemented in a cloud environment familiar to those of skill in the art. In various embodiments, the security analytics system  118  may use data stored in a repository of security analytics data  880  in the performance of certain security analytics operations, described in greater detail herein. Those of skill in the art will recognize that many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
       FIG. 8  is a simplified block diagram showing the mapping of an event to a security vulnerability scenario implemented in accordance with an embodiment of the invention. In certain embodiments, an entity behavior catalog (EBC) system  120  may be implemented to identify a security related activity, described in greater detail herein. In certain embodiments, the security related activity may be based upon an observable, likewise described in greater detail herein. In certain embodiments, the observable may include event information corresponding to electronically-observable behavior enacted by an entity. In certain embodiments, the event information corresponding to electronically-observable behavior enacted by an entity may be received from an electronic data source, such as the EBC data sources  810  shown in  FIGS. 6 a   ,  8 ,  16 , and  17   b.    
     In certain embodiments, as likewise described in greater detail herein, the EBC system  120  may be implemented to identify a particular event of analytic utility by analyzing an associated security related activity. In certain embodiments, the EBC system  120  may be implemented to generate entity behavior catalog data based upon an identified event of analytic utility associated with a particular security related activity. In various embodiments, the EBC system  120  may be implemented to associate certain entity behavior data it may generate with a predetermined abstraction level, described in greater detail herein. 
     In various embodiments, the EBC system  120  may be implemented to use certain EBC data  690  and an associated abstraction level to generate a hierarchical set of entity behaviors  870 , described in greater detail herein. In certain embodiments, the hierarchical set of entity behaviors  870  generated by the EBC system  120  may represent an associated security risk, likewise described in greater detail herein. Likewise, as described in greater detail herein, the EBC system  120  may be implemented in certain embodiments to store the hierarchical set of entity behaviors  870  and associated abstraction level information within a repository of EBC data  690 . In certain embodiments, the repository of EBC data  690  may be implemented to provide an inventory of entity behaviors for use when performing a security operation, likewise described in greater detail herein. 
     Referring now to  FIG. 8 , the EBC system  120  may be implemented in various embodiments to receive certain event information, described in greater detail herein, corresponding to an event associated with an entity interaction. As used herein, event information broadly refers to any information directly or indirectly related to an event. As likewise used herein, an event broadly refers to the occurrence of at least one action performed by an entity. In certain embodiments, the at least one action performed by an entity may include the enactment of an entity behavior, described in greater detail herein. In certain embodiments, the entity behavior may include an entity&#39;s physical behavior, cyber behavior, or a combination thereof, as likewise described in greater detail herein. 
     Likewise, as used herein, an entity interaction broadly refers to an action influenced by another action enacted by an entity. As an example, a first user entity may perform an action, such as sending a text message to a second user entity, who in turn replies with a response. In this example, the second user entity&#39;s action of responding is influenced by the first user entity&#39;s action of sending the text message. In certain embodiments, an entity interaction may include the occurrence of at least one event enacted by one entity when interacting with another, as described in greater detail herein. In certain embodiments, an event associated with an entity interaction may include at least one entity attribute, described in greater detail herein, and at least one entity behavior, likewise described in greater detail herein. 
     In certain embodiments, an entity attribute and an entity behavior may be respectively abstracted to an entity attribute  872  and an entity behavior  874  abstraction level. In certain embodiments, an entity attribute  872  and an entity behavior  874  abstraction level may then be associated with an event  876  abstraction level. In certain embodiments, the entity attribute  872 , entity behavior  874 , and event  876  abstraction levels may in turn be associated with a corresponding entity behavior hierarchy  870 , as described in greater detail herein. 
     In various embodiments, the event information may be received from certain EBC data sources  810 , such as a user  802  entity, an endpoint  804  non-user entity, a network  806  non-user entity, or a system  808  non-user entity. In certain embodiments, one or more events may be associated with a particular entity interaction. As an example, as shown in  FIG. 8 , one or more events i+n  812  may be associated with a user/device  730  interaction between a user  802  entity and an endpoint  904  non-user entity. Likewise, one or more events j+n  814  may be associated with a user/network  742  interaction between a user  802  entity and a network  806  non-user entity. As likewise shown in  FIG. 8 , one or more events k+n  916   816  may be associated with a user/resource  748  interaction between a user  802  entity and a system  808  non-user entity. 
     In certain embodiments, details of an event, such as events i+n  812 , j+n  814 , and k+n  816 , may be included in their associated event information. In various embodiments, as described in greater detail herein, analytic utility detection operations may be performed on such event information to identify events of analytic utility. In various embodiments, certain event information associated with an event determined to be of analytic utility may be used to derive a corresponding observable. As used herein, an observable broadly refers to an event of analytic utility whose associated event information may include entity behavior that may be anomalous, abnormal, unexpected, malicious, or some combination thereof, as described in greater detail herein. 
     As an example, the details contained in the event information respectively corresponding to events i+n  812 , j+n  814 , and k+n  816  may be used to derive observables i+n  822 , j+n  824 , and k+n  826 . In certain embodiments, the resulting observables i+n  822 , j+n  824 , and k+n  826  may then be respectively associated with a corresponding observable  878  abstraction level. In certain embodiments, the observable  878  abstraction level may in turn be associated with a corresponding entity behavior hierarchy  870 , as described in greater detail herein. 
     In certain embodiments, the resulting observables may in turn be processed to generate an associated security related activity. As used herein, a security related activity broadly refers to an abstracted description of an interaction between two entities, described in greater detail herein, which may represent anomalous, abnormal, unexpected, malicious entity behavior. For example, observables i+n  822 , j+n  824 , and k+n  826  may in turn be processed to generate corresponding security related activities i  832 , j  834 , and k  836 . In certain embodiments, the resulting security related activities, i  832 , j  834 , and k  836  may then be respectively associated with a corresponding security related activity  880  abstraction level. In certain embodiments, the security related activity  880  abstraction level may in turn be associated with a corresponding entity behavior hierarchy  870 , as described in greater detail herein. 
     In various embodiments, sessionization and fingerprint generation operations  820 , described in greater detail herein, may be performed to associate certain events, observables, and security related activities, or a combination thereof, with a corresponding session, likewise described in greater detail herein. As an example, events i+n  812 , j+n  814 , k+n  816 , observables i+n  822 , j+n  824 , k+n  826 , and security related activities i  832 , j  834 , k  836  may be associated with corresponding sessions. In certain embodiments, a security related activity may be processed with associated contextual information, described in greater detail herein, to generate a corresponding EBP element. 
     For example, security related activities i  832 , j  834 , and k  836  may be processed with associated contextual information to generate corresponding EBP elements i  842 , j  844 , and k  846 . In various embodiments, the resulting EBP elements i  842 , j  844 , and k  846  may then be associated with a corresponding EBP element  882  abstraction level. In certain embodiments, the EBP element  882  abstraction level may in turn be associated with a corresponding entity behavior hierarchy  870 , as described in greater detail herein. 
     In certain embodiments, EBP generation and modification  840  operations may be performed to associate one or more EBP elements with a particular EBP  638 . As an example, EBP elements i  842 , j  844 , and k  946  may be associated with a particular EBP  638 , which may likewise be respectively associated with the various entities involved in the user/device  730 , user/network  742 , or user/resource  748  interactions. In these embodiments, the method by which the resulting EBP elements i  842 , j  844 , and k  846  are associated with a particular EBP  638  is a matter of design choice. In certain embodiments, the EBP  638  may likewise associated with an EBP  884  abstraction level. In certain embodiments, the EBP  884  abstraction level may in turn be associated with a corresponding entity behavior hierarchy  870 , as described in greater detail herein. 
     In various embodiments, the resulting EBP  638  may be used in the performance of security risk use case association  850  operations to identify one or more security risk use cases that match certain entity behavior information stored in the EBP  638 . As used herein, a security risk use case broadly refers to a set of security related activities that create a security risk narrative that can be used to adaptively draw inferences, described in greater detail herein, from entity behavior enacted by a particular entity. In certain of these embodiments, the entity behavior information may be stored within the EBP  638  in the form of an EBP element, a security related activity, an observable, or an event, or a combination thereof. In certain embodiments, the security risk use case association operations may be performed by the security risk use case management  128  module of the EBC system  120  described in the text associated with  FIG. 6 a   . In certain embodiments, identified security risk use cases may then be associated with a security risk use case  886  abstraction level. In certain embodiments, the security risk use case  886  abstraction level may in turn be associated with a corresponding entity behavior hierarchy  870 , as described in greater detail herein. 
     In certain embodiments, the results of the security risk use case association  850  operations may in turn be used to perform security vulnerability scenario inference  860  operations to associate one or more security risk use cases with one or more security vulnerability scenarios. As used herein, a security vulnerability scenario broadly refers to a grouping of one or more security risk use cases that represent a particular class of security vulnerability. In certain embodiments, the security vulnerability scenario association operations may be performed by the security vulnerability scenario management  126  module of the EBC system  120  described in the text associated with  FIG. 6 a   . In certain embodiments, the associated security vulnerability scenarios may then be associated with a security vulnerability scenario  888  abstraction level. In certain embodiments, the security vulnerability scenario  888  abstraction level may in turn be associated with a corresponding entity behavior hierarchy  870 , as described in greater detail herein. 
     In various embodiments, certain event information associated with events i+n  812 , j+n  814 , and k+n  816  and certain observable information associated with observables i+n  822 , j+n  824 , and k+n  826  may be stored in a repository of EBC data  690 . In various embodiments, certain security related activity information associated with security related activities i  832 , j  834 , and k  836  and EBP elements i  842 , j  844 , and k  846  may likewise be stored in the repository of EBC data  690 . Likewise, in various embodiments, certain security risk use case association and security vulnerability scenario association information respectively associated with the performance of security risk use case association  850  and security vulnerability scenario inference  860  operations may be stored in the repository of EBC data  690 . 
       FIG. 9  is a simplified block diagram of the generation of a session and a corresponding session-based fingerprint implemented in accordance with an embodiment of the invention. In certain embodiments, an observable  906  may be derived from an associated event, as described in greater detail herein. In certain embodiments, one or more observables  906  may be processed to generate a corresponding security related activity  908 . In certain embodiments, one or more security related activities  908  may then be respectively processed to generate a corresponding activity session  910 . In turn, the session  910  may be processed in certain embodiments to generate a corresponding session fingerprint  912 . In certain embodiments, the resulting activity session  910  and its corresponding session fingerprint  912 , individually or in combination, may then be associated with a particular entity behavior profile (EBP) element  980 . In certain embodiments the EBP element  980  may in turn be associated with an EBP  638 . 
     In certain embodiments, intervals in time  904  respectively associated with various security related activities  908  may be contiguous. For example, as shown in  FIG. 9 , the intervals in time  904  associated with observables  906  ‘1’  914  and ‘2’  916  may be contiguous. Accordingly, the intervals in time  904  associated with the security related activities  908  ‘1’  918  and ‘2’  920  respectively generated from observables  906  ‘1’  914  and ‘2’  916  would likewise be contiguous. 
     As likewise shown in  FIG. 9 , the resulting security related activities  908  ‘1’  918  and ‘2’  920  may be processed to generate an associated activity session ‘A’  922 , which then may be processed to generate a corresponding session fingerprint ‘A’  924 . In certain embodiments, activity session ‘A’  922  and its corresponding session fingerprint ‘A’  924  may be used to generate a new entity behavior profile (EBP) element  980  ‘A’  926 . In certain embodiments, EBP element  980  ‘A’  926  generated from activity session  910  ‘A’  922  and its corresponding session fingerprint  912  ‘A’  924  may be associated with an existing EBP  638 . 
     To provide an example, a user may enact various observables  906  ‘1’  914  to update sales forecast files, followed by the enactment of various observables  906  ‘2’  1016  to attach the updated sales forecast files to an email, which is then sent to various co-workers. In this example, the enactment of observables  906  ‘1’  914  and ‘2’  916  result in the generation of security related activities  908  ‘1’  918  and ‘2’  920 , which in turn are used to generate activity session  910  ‘A’  922 . In turn, the resulting activity session  910  ‘A’  922  is then used to generate its corresponding session-based fingerprint  912  ‘A’  924 . To continue the example, activity session  910  ‘A’  922  is associated with security related activities  908  ‘1’  918  and ‘2’  920 , whose associated intervals in time  904  are contiguous, as they are oriented to the updating and distribution of sales forecast files via email. 
     Various aspects of the invention reflect an appreciation that a user may enact certain entity behaviors on a recurring basis. To continue the preceding example, a user may typically update sales forecast files and distribute them to various co-workers every morning between 8:00 AM and 10:00 AM. Accordingly, the activity session  910  associated with such a recurring activity may result in a substantively similar session fingerprint  912  week-by-week. However, a session fingerprint  912  for the same session  910  may be substantively different should the user happen to send an email with an attached sales forecast file to a recipient outside of their organization. Consequently, a session fingerprint  912  that is inconsistent with session fingerprints  912  associated with past activity sessions  910  may indicate anomalous, abnormal, unexpected or malicious behavior. 
     In certain embodiments, two or more activity sessions  910  may be noncontiguous, but associated. In certain embodiments, an activity session  910  may be associated with two or more sessions  910 . In certain embodiments, an activity session  910  may be a subset of another activity session  910 . As an example, as shown in  FIG. 9 , the intervals in time  904  respectively associated with observables  906  ‘3’  914  and ‘6’  932  may be contiguous. Likewise, the intervals in time  904  associated with observables  906  ‘4’  936  and ‘5’  938  may be contiguous. 
     Accordingly, the intervals in time  904  associated with the security related activities  908  ‘4’  936  and ‘5’  938  respectively generated from observables  906  ‘4’  928  and ‘5’  930  would likewise be contiguous. However, the intervals in time  904  associated with security related activities  908  ‘4’  936  and ‘5’  938  would not be contiguous with the intervals in time respectively associated with security related activities  908  ‘3’  934  and ‘6’  940 . 
     As likewise shown in  FIG. 9 , the resulting security related activities  908  ‘3’  934  and ‘6’  940  may be respectively processed to generate corresponding sessions ‘B’  942  and ‘D’  946 , while security related activities  908  ‘4’  936  and ‘5’  938  may be processed to generate activity session  910  ‘C’  944 . In turn, activity sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946  are then respectively processed to generate corresponding session-based fingerprints  912  ‘B’  948 , ‘C’  950  and ‘D’  952 . 
     Accordingly, the intervals of time  904  respectively associated with activity sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946 , and their corresponding session fingerprints  912  ‘B’  948 , ‘C’  950  and ‘D’  952 , are not contiguous. Furthermore, in this example activity sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946 , and their corresponding session fingerprints  912  ‘B’  948 , ‘C’  950  and ‘D’  952 , are not associated with the EBP  638 . Instead, as shown in  FIG. 9 , activity sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946  are processed to generate activity session  910  ‘E’  954  and session fingerprints  912  ‘B’  948 , ‘C’  950  and ‘D’  952  are processed to generate session fingerprint  912  ‘E’  956 . In certain embodiments, activity session ‘E’  954  and its corresponding session fingerprint ‘E’  956  may be used to generate a new EBP element  980  ‘E’  958 . In certain embodiments, EBP element  980  ‘E’  958  generated from activity session  910  ‘E’  954  and its corresponding session fingerprint  912  ‘E’  956  may be associated with an existing EBP  638 . 
     Accordingly, session  910  ‘E’  1054  is associated with activity sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946 . Likewise, sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946  are subsets of session  910  ‘E’  954 . Consequently, while the intervals of time respectively associated with activity sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946 , and their corresponding session fingerprints  912  ‘B’  948 , ‘C’  950  and ‘D’  952  may not be contiguous, they are associated as they are respectively used to generate session  910  ‘E’  954  and its corresponding session fingerprint  912  ‘E’  1056 . 
     To provide an example, a user plans to attend a meeting scheduled for 10:00 AM at a secure facility owned by their organization to review a project plan with associates. However, the user wishes to arrive early to prepare for the meeting. Accordingly, they arrive at 9:00 AM and use their security badge to authenticate themselves and enter the facility. In this example, the enactment of observables  906  ‘3’  926  may correspond to authenticating themselves with their security badge and gaining access to the facility. As before, observables  906  ‘3’  926  may be used to generate a corresponding security related activity  908  ‘3’  934 . In turn, the security related activity  908  ‘3’  934  may then be used to generate session  910  ‘B’  942 , which is likewise used in turn to generate a corresponding session fingerprint  912  ‘B’  948 . 
     The user then proceeds to a conference room reserved for the meeting scheduled for 10:00 AM and uses their time alone to prepare for the upcoming meeting. Then, at 10:00 AM, the scheduled meeting begins, followed by the user downloading the current version of the project plan, which is then discussed by the user and their associate for a half hour. At the end of the discussion, the user remains in the conference room and spends the next half hour making revisions to the project plan, after which it is uploaded to a datastore for access by others. 
     In this example, observables  906  ‘4’  928  may be associated with the user downloading and reviewing the project plan and observables  906  ‘5’  930  may be associated with the user making revisions to the project plan and then uploading the revised project plan to a datastore. Accordingly, behavior elements  906  ‘4’  928  and ‘5’  930  may be respectively used to generate security related activities  908  ‘4’  936  and ‘5’  938 . In turn, the security related activities  908  ‘4’  936  and ‘5’  938  may then be used to generate session  910  ‘C’  944 , which may likewise be used in turn to generate its corresponding session-based fingerprint  912  ‘C’  950 . 
     To continue the example, the user may spend the next half hour discussing the revisions to the project plan with a co-worker. Thereafter, the user uses their security badge to exit the facility. In continuance of this example, observables  906  ‘6’  932  may be associated with the user using their security badge to leave the secure facility. Accordingly, observables  906  ‘6’  932  may be used to generate a corresponding security related activity  908  ‘6’  940 , which in turn may be used to generate a corresponding session  910  ‘D’  946 , which likewise may be used in turn to generate a corresponding session fingerprint  912  ‘D’  952 . 
     In this example, the intervals of time  904  respectively associated with activity sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946 , and their corresponding session fingerprints  912  ‘B’  948 , ‘C’  950 , and ‘D’  952 , are not contiguous. However they may be considered to be associated as their corresponding observables  906  ‘3’  926 , ‘4’  928 , ‘5’  930 , and ‘6’  932 , all have the common attribute of having been enacted within the secure facility. Furthermore, security related activities  908  ‘4’  936  and ‘5’  938  may be considered to be associated as their corresponding observables  906  have the common attribute of being associated with the project plan. 
     Accordingly, while the intervals of time  904  respectively associated with activity sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946 , and their corresponding session-based fingerprints  912  ‘B’  948 , ‘C’  950 , and ‘D’  952 , may not be contiguous, they may be considered to be associated. Consequently, sessions  910  ‘B’  942 , ‘C’  944 , and ‘D’  946  may be considered to be a subset of session  910  ‘E’  954  and session-based fingerprints  912  ‘B’  948 , ‘C’  950 , and ‘D’  952  may be considered to be a subset of session-based fingerprint  912  ‘E’  956 . 
     In certain embodiments, the interval of time  904  corresponding to a first activity session  910  may overlap an interval of time  904  corresponding to a second activity session  910 . For example, observables  906  ‘7’  958  and ‘8’  960  may be respectively processed to generate security related activities  908  ‘7’  962  and ‘8’  964 . In turn, the resulting security related activities  908  ‘7’  962  and ‘8’  964  are respectively processed to generate corresponding activity sessions  910  ‘F’  966  and ‘G’  968 . Sessions The resulting activity sessions  910  ‘F’  966  and ‘G’  968  are then respectively processed to generate corresponding session-based fingerprints  912  ‘F’  970  and ‘G’  972 . 
     However, in this example activity sessions  910  ‘F’  966  and ‘G’  968 , and their corresponding session fingerprints  912  ‘F’  970  and ‘G’  972 , are not associated with the EBP  638 . Instead, as shown in  FIG. 9 , activity sessions  910  ‘F’  966  and ‘G’  968  are processed to generate activity session  910  ‘E’  954  and session fingerprints  912  ‘F’  970  and ‘G’  972  are processed to generate session fingerprint  912  ‘H’  976 . In certain embodiments, activity session ‘H’  974  and its corresponding session fingerprint ‘H’  976  may be used to generate a new EBP element  980  ‘H’  978 . In certain embodiments, EBP element  980  ‘H’  978  generated from activity session  910  ‘E’  974  and its corresponding session fingerprint  912  ‘E’  976  may be associated with an existing EBP  638 . 
     Accordingly, the time  904  interval associated with activity session  910  ‘F’  966  and its corresponding session fingerprint  912  ‘F’  970  overlaps with the time interval  904  associated with activity session  910  ‘G’  968  and its corresponding session fingerprint  912  ‘G’  972 . As a result, activity sessions  910  ‘F’  966  and ‘G’  968  are subsets of activity session  910  ‘H’  974 . Consequently, while the intervals of time respectively associated with activity sessions  910  ‘F’  966  and ‘G’  968 , and their corresponding session fingerprints  912  ‘F’  970  and ‘G’  972  may overlap, they are associated as they are respectively used to generate activity session  910  ‘H’  974  and its corresponding session fingerprint  912  ‘H’  976 . 
     To provide an example, a user may decide to download various images for placement in an online publication. In this example, observables  906  ‘7’  958  may be associated with the user iteratively searching for, and downloading, the images they wish to use in the online publication. However, the user may not begin placing the images into the online publication until they have selected and downloaded the first few images they wish to use. 
     To continue the example, observables  906  ‘8’ may be associated with the user placing the downloaded images in the online publication. Furthermore, the placement of the downloaded images into the online publication may begin a point in time  904  subsequent to when the user began to download the images. Moreover, the downloading of the images may end at a point in time  904  sooner than when the user completes the placement of the images in the online publication. 
     In continuance of the example, observables  906  ‘7’  958  and ‘8’  960  may be respectively processed to generate security related activities  908  ‘7’  962  and ‘8’  964 , whose associated intervals of time  904  overlap one another. Accordingly, the intervals in time  904  associated with activity sessions  910  ‘F’  966  and ‘G’  968  will likewise overlap one another as they are respectively generated from security related activities  908  ‘7’  962  and ‘8’  964 . 
     Consequently, while the intervals of time  904  respectively associated with activity sessions  910  ‘F’  966  and ‘G’  968 , and their corresponding session fingerprints  912  ‘F’  970  and ‘G’  972 , may overlap, they may be considered to be associated as they both relate to the use of images for the online publication. Accordingly, activity sessions  910  ‘F’  1066  and ‘G’  968  may be considered to be a subset of activity session  910  ‘H’  974  and session fingerprints  912  ‘F’  970  and ‘G’  972  may be considered to be a subset of session fingerprint  912  ‘H’  976 . 
       FIG. 10  is a generalized flowchart of session fingerprint generation operations performed in accordance with an embodiment of the invention. In this embodiment, activity session fingerprint generation operations are begun in step  1002 , followed by the selection of an entity in step  1004  for associated entity behavior profile (EBP) element generation. As used herein, an EBP element broadly refers to any data element stored in an EBP, as described in greater detail herein. In various embodiments, an EBP element may be used to describe a particular aspect of an EBP, such as certain entity behaviors enacted by an entity associated with the EBP. Ongoing monitoring operations are then performed in step  1006  to monitor the selected entity&#39;s behavior to detect the occurrence of an event, described in greater detail herein. 
     A determination is then made in step  1008  whether an event has been detected. If not, then a determination is made in step  1026  whether to continue monitoring the entity&#39;s behavior to detect an event. If so, then the process is continued, proceeding with step  1006 . Otherwise, session fingerprint generation operations are ended in step  1028 . However, if it was determined in step  1008  that an event was detected, then event data associated with the detected event is processed to determine whether the event is of analytic utility, as described in greater detail herein. 
     A determination is then made in step  1012  to determine whether the event is of analytic utility. If not, then the process is continued, proceeding with  1026 . Otherwise, an observable, described in greater detail herein, is derived from the event in step  1014 . The resulting observable is then processed with associated observables in step  1016 , as likewise described in greater detail herein, to generate a security related activity. As likewise described in greater detail herein, the resulting security related activity is then processed in step  1018  with associated security related activities to generate an activity session. 
     In turn, the resulting activity session is then processed in step  1020  to generate a corresponding session fingerprint. The resulting session fingerprint is then processed with its corresponding activity session in step  1022  to generate an associated EBP element. The resulting EBP element is then added to an EPB associated with the entity in step  1024  and the process is then continued, proceeding with step  1026 . 
       FIG. 11  is simplified block diagram of process flows associated with the operation of an entity behavior catalog (EBC) system implemented in accordance with an embodiment of the invention. In certain embodiments, the EBC system  120  may be implemented to define and manage an entity behavior profile (EBP)  638 , as described in greater detail herein. In certain embodiments, the EBP  638  may be implemented to include a user entity profile  602 , a user entity mindset profile  632 , a non-user entity profile  634 , and an entity state  636 , or a combination thereof, as likewise described in greater detail herein. 
     In certain embodiments, the EBC system  120  may be implemented use a particular user entity profile  602  in combination with a particular entity state  638  to generate an associated user entity mindset profile  632 , likewise as described in greater detail herein. In certain embodiments, the EBC system  120  may be implemented to use the resulting user entity mindset profile  632  in combination with its associated user entity profile  602 , non-user entity profile  634 , and entity state  638 , or a combination thereof, to detect entity behavior of analytic utility. In various embodiments, the EBC system  120  may be implemented to perform EBP management  1124  operations to process certain entity attribute and entity behavior information, described in greater detail herein, associated with defining and managing an EBP  638 . In various embodiments, the EBC management operations  1124  may be performed by the EBP management  124  module described in the descriptive text associated with  FIG. 6   a.    
     As used herein, entity attribute information broadly refers to information associated with a particular entity. In various embodiments, the entity attribute information may include certain types of content. In certain embodiments, such content may include text, unstructured data, structured data, graphical images, photographs, audio recordings, video recordings, biometric information, and so forth. In certain embodiments, the entity attribute information may include metadata. In certain embodiments, the metadata may include entity attributes, which in turn may include certain entity identifier types or classifications. 
     In certain embodiments, the entity attribute information may include entity identifier information. In various embodiments, the EBC system  120  may be implemented to use certain entity identifier information to ascertain the identity of an associated entity at a particular point in time. As used herein, entity identifier information broadly refers to an information element associated with an entity that can be used to ascertain or corroborate the identity of its corresponding entity at a particular point in time. In certain embodiments, the entity identifier information may include user authentication factors, user entity  602  and non-user entity  634  profile attributes, user and non-user entity behavior factors, user entity mindset factors, information associated with various endpoint and edge devices, networks, and resources, or a combination thereof. 
     In certain embodiments, the entity identifier information may include temporal information. As used herein, temporal information broadly refers to a measure of time (e.g., a date, timestamp, etc.), a measure of an interval of time (e.g., a minute, hour, day, etc.), or a measure of an interval of time (e.g., two consecutive weekdays days, or between Jun. 3, 2017 and Mar. 4, 2018, etc.). In certain embodiments, the temporal information may be associated with an event associated with a particular point in time. As used herein, such a temporal event broadly refers to an occurrence, action or activity enacted by, or associated with, an entity at a particular point in time. 
     Examples of such temporal events include making a phone call, sending a text or an email, using a device, such as an endpoint device, accessing a system, and entering a physical facility. Other examples of temporal events include uploading, transferring, downloading, modifying, or deleting data, such as data stored in a datastore, or accessing a service. Yet other examples of temporal events include interactions between two or more users, interactions between a user and a device, interactions between a user and a network, and interactions between a user and a resource, whether physical or otherwise. Yet still other examples of temporal events include a change in name, address, physical location, occupation, position, role, marital status, gender, association, affiliation, or assignment. 
     As likewise used herein, temporal event information broadly refers to temporal information associated with a particular event. In various embodiments, the temporal event information may include certain types of content. In certain embodiments, such types of content may include text, unstructured data, structured data, graphical images, photographs, audio recordings, video recordings, and so forth. In certain embodiments, the temporal event information may include metadata. In various embodiments, the metadata may include temporal event attributes, which in turn may include certain entity identifier types or classifications, described in greater detail herein. 
     In certain embodiments, the EBC system  120  may be implemented to use information associated with such temporal resolution of an entity&#39;s identity to assess the risk associated with a particular entity, at a particular point in time, and respond with a security operation  1128 , described in greater detail herein. In certain embodiments, the EBC system  120  may be implemented to respond to such assessments in order to reduce operational overhead and improve system efficiency while maintaining associated security and integrity. In certain embodiments, the response to such assessments may be performed by a security administrator. Accordingly, certain embodiments of the invention may be directed towards assessing the risk associated with the affirmative resolution of the identity of an entity at a particular point in time in combination with its behavior and associated contextual information. Consequently, the EBC system  120  may be more oriented in various embodiments to risk adaptation than to security administration. 
     Referring now to  FIG. 11 , in certain embodiments, EBC system  120  operations are begun with the receipt of information associated with an initial event i  1102 . In certain embodiments, information associated with an initial event i  1102  may include user entity profile  602  attributes, user behavior factors, user entity mindset factors, entity state information, contextual information, all described in greater detail herein, or a combination thereof. In various embodiments, certain user entity profile  602  data, user entity mindset profile  632  data, non-user entity profile  634  data, entity state  636  data, contextual information, and temporal information stored in a repository of EBC data  690  may be retrieved and then used to perform event enrichment  1108  operations to enrich the information associated with event i  1102 . In certain embodiments, event enrichment  1108  operations may be performed by the event enrichment  680  module of the EBC system  120  described in the text associated with  FIG. 6   a.    
     Analytic utility detection  1112  operations are then performed on the resulting enriched event i  1102  to determine whether it is of analytic utility. If so, then it is derived as an observable  906 , described in greater detail herein. In certain embodiments, event i+1  1104  through event i+n  1106 , may in turn be received by the EBC system  120  and be enriched  1008 . Analytic utility detection  1112  operations are then performed on the resulting enriched event i+1  1104  through event i+n  1106  to determine whether they are of analytic utility. Observables  906  are then derived from those that are. In certain embodiments, the analytic utility detection  1112  operations may be performed by the analytic utility detection module  682  of the EBC system  120  described in the text associated with  FIG. 6   a.    
     In certain embodiments, security related activity abstraction  1114  operations may be performed on the resulting observables  906  corresponding to events i  1102 , i+1  1104 , i+n  1106  to generate an associated security related activity  908 , described in greater detail herein. In various embodiments, a security related activity  908  may be expressed in a Subject Action Object format and associated with observables  906  resulting from event information provided by various received from certain EBC data sources, likewise described in greater detail herein. In certain embodiments, a security related activity abstraction  1114  operation may be performed to abstract away EBC data source-specific knowledge and details when expressing an entity behavior. For example, rather than providing the details associated with a “Windows:4624” non-user entity event, its details may be abstracted to “User Login To Device” security related activity  908 . 
     In various embodiments, sessionization and fingerprint  820  operations, described in greater detail herein, may be performed on event information corresponding to events i  1102 , i+1  1104 , i+n  1106 , their corresponding observables  906 , and their associated security related activities  908 , or a combination thereof, to generate session information. In various embodiments, the resulting session information may be used to associate certain events i  1102 , i+1  1104 , i+n  1106 , or their corresponding observables  906 , or their corresponding security related activities  908 , or a combination thereof, with a particular session. 
     In certain embodiments, as likewise described in greater detail herein, one or more security related activities  908  may in turn be associated with a corresponding EBP element. In various embodiments, the previously-generated session information may be used to associate the one or more security related activities  908  with a particular EBP element. In certain embodiments, the one or more security related activities  908  may be associated with its corresponding EBP element through the performance of an EBP management  1124  operation. Likewise, in certain embodiments, one or more EBP elements may in turn be associated with the EBP  638  through the performance of an EBP management  1124  operation. In certain embodiments, the EBP management  1024  operations may be performed by the EBP management  124  module of the EBC system  120  described in the text associated with  FIG. 6   a.    
     In various embodiments, certain contextualization information stored in the repository of EBC data  690  may be retrieved and then used to perform entity behavior contextualization  1118  operations to provide entity behavior context, based upon the entity&#39;s user entity profile  602 , or non-user entity profile  634 , and its associated entity state  638 . In certain embodiments, the entity behavior contextualization  1118  operations may be performed by the entity behavior contextualization module  684  of the EBC system  120 , described in the text associated with  FIG. 6 a   . In various embodiments, security risk use case association  1118  operations may be performed to associate an EBP  638  with a particular security risk use case. In certain embodiments, the results of the previously-performed entity behavior contextualization  1118  operations may be used to perform the security risk use case association  850  operations. In certain embodiments, the security risk use case association  850  operations may be performed by the security risk use case management  128  module of the EBC system  120  described in the text associated with  FIG. 6   a.    
     In various embodiments, security vulnerability scenario inference  860  operations may be performed to associate a security risk use case with a particular security vulnerability scenario, described in greater detail herein. In various embodiments, certain observables  906  derived from events of analytical utility may be used to perform the security vulnerability scenario inference  860  operations. In various embodiments, certain entity behavior contexts resulting from the performance of the entity behavior contextualization  1118  operations may be used to perform the security vulnerability scenario inference  860  operations. In certain embodiments, the security vulnerability scenario inference  860  operations may be performed by the security vulnerability scenario management module  126  of the EBC system  120  described in the text associated with  FIG. 6   a.    
     In certain embodiments, entity behavior meaning derivation  1126  operations may be performed on the security vulnerability behavior scenario selected as a result of the performance of the security vulnerability scenario inference  860  operations to derive meaning from the behavior of the entity. In certain embodiments, the entity behavior meaning derivation  1126  operations may be performed by analyzing the contents of the EBP  638  in the context of the security vulnerability behavior scenario selected as a result of the performance of the security vulnerability scenario inference  860  operations. In certain embodiments, the derivation of entity behavior meaning may include inferring the intent of an entity associated with event i  1102  and event i+1  1104  through event i+n  1106 . In certain embodiments, the entity behavior meaning derivation  1126  operations may be performed by the entity behavior meaning derivation module  686  of the EBC system  120  described in the text associated with  FIG. 6 . 
     In various embodiments, performance of the entity behavior meaning derivation  1126  operations may result in the performance of a security operation  1128 , described in greater detail herein. In certain embodiments, the security operation  1128  may include a cyber kill chain  1130  operation, or a risk-adaptive protection  1132  operation, or both. In certain embodiments, the cyber kill chain  1130  operation may be performed to disrupt the execution of a cyber kill chain, described in greater detail herein. In certain embodiments, the risk-adaptive protection  1132  operation may include adaptively responding with an associated risk-adaptive response, as described in greater detail herein. 
     In various embodiments, the security operation  1128  may include certain risk mitigation operations being performed by a security administrator. As an example, performance of the security operation  1128  may result in a notification being sent to a security administrator alerting them to the possibility of suspicious behavior. In certain embodiments, the security operation  1128  may include certain risk mitigation operations being automatically performed by a security analytics system or service. As an example, performance of the security operation  1128  may result in a user&#39;s access to a particular system being disabled if an attempted access occurs at an unusual time or from an unknown device. 
     In certain embodiments, meaning derivation information associated with event i  1102  may be used to update the user entity profile  602  or non-user entity profile  634  corresponding to the entity associated with event i  1102 . In certain embodiments, the process is iteratively repeated, proceeding with meaning derivation information associated with event i+1  1104  through event i+n  1106 . From the foregoing, skilled practitioners of the art will recognize that a user entity profile  602 , or a non-user entity profile  634 , or the two in combination, as implemented in certain embodiments, not only allows the identification of events associated with a particular entity that may be of analytic utility, but also provides higher-level data that allows for the contextualization of observed events. Accordingly, by viewing individual sets of events both in context and with a view to how they may be of analytic utility, it is possible to achieve a more nuanced and higher-level comprehension of an entity&#39;s intent. 
       FIG. 12  is a table showing components of an entity behavior profile (EBP) implemented in accordance with an embodiment of the invention. In various embodiments, an EBP  638  may be implemented to certain include entity attributes  1204  behavioral models  1206 , and inferences  1208 , along with entity state  636 . In certain embodiments, an EBP&#39;s  638  entity state  636  may be short-term, or reflect the state of an entity at a particular point or interval in time. In certain embodiments, an EBP&#39;s  638  entity state  636  may be long-term, or reflect the state of an entity at recurring points or intervals in time. 
     In certain embodiments, an EBP&#39;s  638  associated entity attributes  1204  may be long-lived. As an example, a particular user entity may have a name, an employee ID, an assigned office, and so forth, all of which are facts rather than insights. In certain embodiments, a particular entity state  636  may be sufficiently long-termed to be considered an entity attribute  1204 . As an example, a first user and a second user may both have an entity state  636  of being irritable. However, the first user may have a short-term entity state  636  of being irritable on an infrequent basis, while the second user may have a long-term entity state  636  of be irritable on a recurring basis. In this example, the long-term entity state  636  of the second user being irritable may be considered to be an entity attribute  1204 . In various embodiments, the determination of what constitutes an entity state  636  and an entity attribute  1204  is a matter of design choice. In certain embodiments, various knowledge representation approaches may be implemented in combination with an entity behavior catalog (EBC) system to understand the ontological interrelationship of entity attributes  1104  one or more EBP&#39;s  638  may contain. In these embodiments, the method by which certain entity attributes  1204  are selected to be tracked by an EBC system, and the method by which they are managed within a corresponding EBP  638 , is a matter of design choice. 
     In certain embodiments, the ATP  638  evolves over time as new events and entity behavior is detected. In certain embodiments, an ATP&#39;s  638  associated behavioral models  1206 , and thus the ATP  638  itself may evolve over time. In certain embodiments, an ATP&#39;s  638  behavioral models  1206  may be used by an ATP system to provide insight into how unexpected a set of events may be. As an example, a behavioral model  1206  may include information related to where a particular user entity works, which devices they may use and locations they may login from, who they may communicate with, and so forth. Certain embodiments of the invention reflect an appreciation that such behavioral models  1206  can be useful when comparing observed user and non-user entity behaviors to past observations in order to determine how unusual a particular entity behavior may be. 
     For example, a user may have more than one EBP  638  associated with a particular channel, which as used herein broadly refers to a medium capable of supporting the electronic observation of a user or non-user behavior, such as a keyboard, a network, a video stream, and so forth. To continue the example, the user may have a particular set of people he sends emails to from his desktop computer, and does so in an orderly and methodical manner, carefully choosing his words, and writing longer than average messages compared to his peers. Consequently, analysis of such an email message will likely indicate it was authored by the user and not someone else. 
     However, the same user may also send emails from a second channel, which is his mobile telephone. When using his mobile telephone, the user&#39;s emails are typically short, contains typos and emojis, and his writing style is primarily limited to simple confirmations or denials. Consequently, analysis of one such email would likely not reveal whether the user was the author or not, due to its brevity. Accordingly, the use of the same channel, which in this example is email, demonstrates the use of different devices will likely generate different behavioral models  1206 , which in turn could affect the veracity of associated inferences  1208 . 
     In certain embodiments, a behavioral model  1206  may be implemented as a session-based fingerprint. As used herein, a session-based fingerprint broadly refers to a unique identifier of an enactor of user or non-user behavior associated with a session. In certain embodiments, the session-based fingerprint may be implemented to determine how unexpected an event may be, based upon an entity&#39;s history as it relates to the respective history of their peer entities. In certain embodiments, the session-based fingerprint may be implemented to determine whether an entity associated with a particular session is truly who they or it claims to be or if they are being impersonated. In certain embodiments, the session-based fingerprint may be implemented to determine whether a particular event, or a combination thereof, may be of analytic utility. In certain embodiments, the session-based fingerprint may include a risk score, be used to generate a risk score, or a combination thereof. 
     As likewise used herein, a fingerprint, as it relates to a session, broadly refers to a collection of information providing one or more distinctive, characteristic indicators of the identity of an enactor of one or more corresponding user or non-user entity behaviors during the session. In certain embodiments, the collection of information may include one or more user or non-user profile elements. A user or non-user profile element, as used herein, broadly refers to a collection of user or non-user entity behavior elements, described in greater detail herein. 
     As used herein, inferences  1208  broadly refer to things that can be inferred about an entity based upon observations. In certain embodiments the observations may be based upon electronically-observable behavior, described in greater detail herein. In certain embodiments, the behavior may be enacted by a user entity, a non-user entity, or a combination thereof. In certain embodiments, inferences  1108  may be used to provide insight into a user entity&#39;s mindset or affective state. 
     As an example, an inference  1208  may be made that a user is unhappy in their job or that they are facing significant personal financial pressures. Likewise, based upon the user&#39;s observed behavior, an inference  1208  may be made that they are at a higher risk of being victimized by phishing schemes due to a propensity for clicking on random or risky website links. In certain embodiments, such inferences  1208  may be implemented to generate a predictive quantifier of risk associated with an entity&#39;s behavior. 
     In certain embodiments, entity state  636 , described in greater detail herein, may be implemented such that changes in state can be accommodated quickly while reducing the overall volatility of a particular EBP  638 . As an example, a user may be traveling by automobile. Accordingly, the user&#39;s location is changing quickly. Consequently, location data is short-lived. As a result, while the location of the user may not be updated within their associated EBP  638  as it changes, the fact their location is changing may prove to be useful in terms of interpreting other location-based data from other sessions. To continue the example, knowing the user is in the process of changing their location may assist in explaining why the user appears to be in two physical locations at once. 
       FIG. 13  is an activities table showing analytic utility actions occurring during a session implemented in accordance with an embodiment of the invention. In certain embodiments, an entity behavior catalog (EBC) system, described in greater detail herein, may be implemented to capture and record various entity actions  1304  enacted by an entity during a session  1302 , likewise described in greater detail herein. In certain embodiments, the actions, and their associated sessions, may be stored in an entity behavior profile (EBP) corresponding to a particular entity. In various embodiments, the EBC system may be implemented to process information stored in an EBP to determine, as described in greater detail herein, which actions  1304  enacted by a corresponding entity during a particular session  1302  may be of analytic utility  1308 . 
     Certain embodiments of the invention reflect an appreciation that multiple sessions  1302 , each of which may be respectively associated with a corresponding entity, may occur within the same interval of time  1306 . Certain embodiments of the invention likewise reflect an appreciation that a single action of analytic utility  1308  enacted by an entity occurring during a particular interval of time  1306  may not appear to be suspicious behavior by an associated entity. Likewise, certain embodiments of the invention reflect an appreciation that the occurrence of multiple actions of analytic utility  1308  enacted by an entity during a particular session  1302  may be an indicator of suspicious behavior. 
     Certain embodiments reflect an appreciation that a particular entity may be associated with two or more sessions  1302  that occur concurrently over a period of time  1306 . Certain embodiments of the invention likewise reflect an appreciation that a single action of analytic utility  1308  enacted by an entity occurring during a first session  1302  may not appear to be suspicious. Conversely, certain embodiments of the invention reflect an appreciation that multiple actions of analytic utility  1308  during a second session  1302  may be an indicator of suspicious behavior. 
     As an example, a user may log into the same system from two different IP addresses, one associated with their laptop computer and the other their mobile phone. In this example, entity actions  1204   1304  enacted by the user using their laptop computer may be associated with a first session  1302  (e.g. session ‘2’), and entity actions  1304  enacted by the user using their mobile phone may be associated with a second session  1202   1302  (e.g., session ‘3’). To continue the example, only one action of analytic utility  1308  may be associated with the first session  1302 , while three actions of analytic utility  1308  may be associated with the second session  1302 . Accordingly, it may be inferred the preponderance of actions of analytic utility  1308  enacted by the user during the second session  1302  may indicate suspicious behavior being enacted with their mobile phone. 
       FIG. 14  shows a simplified block diagram of the components of a cyber kill chain associated with a security operation performed in accordance with an embodiment of the invention. In certain embodiments, a security analytics system, or an entity behavior catalog (EBC) system, or both, may be implemented to monitor the behavior of a particular entity, as described in greater detail herein. In certain embodiments, such monitoring may include observing an electronically-observable data source, such as the EBC data sources shown in  FIGS. 6 a   ,  8 ,  16 , and  17   b.    
     In certain embodiments, an observable, described in greater detail herein, may be derived from the electronically-observable data source. In certain embodiments, the observable is associated with an event of analytic utility, likewise described in greater detail herein. In certain embodiments, one or more derived observables may then be associated with a security related activity, as described in greater detail herein. In various embodiments, a particular security activity may be associated with a component of a cyber kill chain. 
     Skilled practitioners of the art will be familiar with a kill chain, which was originally used as a military concept related to the structure of an attack. In general, the phases of a military kill chain consisted of target identification, force dispatch to target, decision and order to attack the target, and destruction of the target. Conversely, breaking or disrupting an opponent&#39;s kill chain is a method of defense or preemptive action. 
     Those of skill in the art will likewise be familiar with a cyber kill chain, developed by the Lockheed Martin company of Bethesda, Md., which is an adaptation of the military kill chain concept that is commonly used to trace the stages of a cyberattack. In general, such stages typically consist of, as shown in  FIG. 14 , data reconnaissance  1402 , data access  1404 , data collection  1406 , data stockpiling  1408 , and data exfiltration  1410 . However, the cyber kill chain concept is not limited to data exfiltration  1410 . It can also be implemented to facilitate the anticipation and recognition of insider threats, social engineering, advanced ransomware, and innovative attacks as they evolve. 
     In certain embodiments, the cyber kill chain may be implemented to anticipate, recognize, and respond to entity behavior of analytic utility that may be determined to be anomalous, abnormal, unexpected, malicious, or some combination thereof, as described in greater detail herein. In certain embodiments, the response to recognition of a kill chain may be to perform an associated security operation, described in greater detail herein. In certain embodiments, the performance of the security operation may result in disrupting or otherwise interfering with the performance, or execution, of one or more components, steps, or phases of a cyber kill chain by affecting the performance of the security related activity by its associated entity. 
     In certain embodiments, a cyber kill chain may consist of more components, step, or phases than those shown in  FIG. 14 . For example, in certain embodiments, the cyber kill chain may likewise include intrusion, exploitation, privilege escalation, lateral movement, obfuscation/anti-forensics, and denial of service (DoS). In such embodiments, the data reconnaissance component  1402  may be executed as an observation stage to identify targets, as well as possible tactics for the attack. In certain embodiments, the data reconnaissance component  1402  may not be limited to data exfiltration. For example, it may be related to other anomalous, abnormal, unexpected, malicious activity, such as identity theft. 
     In certain embodiments, the data access  1404  component may not be limited to gaining access to data. In certain embodiments, the data access  1404  component of a cyber kill chain may be executed as an intrusion phase. In such embodiments, the attacker may use what was learned in execution of the data reconnaissance  1402  component to determine how to gain access to certain systems, possibly through the use of malware or exploitation of various security vulnerabilities. In certain embodiments, a cyber kill chain may likewise include an exploitation component, which may include various actions and efforts to deliver malicious code and exploit vulnerabilities in order to gain a better foothold with a system, network, or other environment. 
     In certain embodiments, a cyber kill chain may likewise include a privilege escalation component, which may include various actions and efforts to escalate the attacker&#39;s privileges in order to gain access to more data and yet more permissions. In various embodiments, a cyber kill chain may likewise include a lateral movement component, which may include moving laterally to other systems and accounts to gain greater leverage. In certain of these embodiments, the leverage may include gaining access to higher-level permissions, additional data, or broader access to other systems. 
     In certain embodiments, a cyber kill chain may likewise include an obfuscation/anti-forensics component, which may include various actions and efforts used by the attacker to hide or disguise their activities. Known obfuscation/anti-forensics approaches include laying false trails, compromising data, and clearing logs to confuse or slow down security forensics teams. In certain embodiments, the data collection  1406  of a cyber kill chain may the collection of data with the intent of eventually being able to exfiltrate it. In certain embodiments, collected data may be accumulated during a data stockpiling  1408  component of a cyber kill chain. 
     In certain embodiments, a cyber kill chain may likewise include a denial of service (DoS) component, which may include various actions and efforts on the part of an attacker to disrupt normal access for users and systems. In certain embodiments, such disruption may be performed to stop a cyberattack from being detected, monitored, tracked, or blocked. In certain embodiments, the data exfiltration  1410  component of a cyber kill chain may include various actions and efforts to get data out of a compromised system. 
     In certain embodiments, information associated with the execution of a particular cyber kill chain may be associated with a corresponding security vulnerability scenario, described in greater detail herein. In certain embodiments, one of more components of a particular cyber kill chain may be associated with one or more corresponding security related use cases, likewise described in greater detail herein. In certain embodiments, performance or execution of a component of a cyber kill chain may be disrupted by affecting completion of the risk use case. Those of skill in the art will recognize that many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope, or intent of the invention. 
       FIGS. 15 a  and 15 b    are a generalized flowchart of the performance of entity behavior catalog (EBC) system operations implemented in accordance with an embodiment of the invention. In this embodiment, EBC system  120  operations are begun with ongoing operations being performed by the EBC system in step  1504  to monitor the receipt of event information to detect the occurrence of an event, described in greater detail herein. 
     A determination is then made in step  1506  to determine whether an event has been detected. If not, then a determination is made in step  1536  to determine whether to continue monitoring the receipt of event information. If so, then the process is continued, proceeding with step  1504 . If not, then a determination is made in step  1540  whether to end EBC system operations. If not, then the process is continued, proceeding with step  1504 . Otherwise EBC system operations are ended in step  1542 . 
     However, if it was determined in step  1506  that an event was detected, then event enrichment operations, described in greater detail herein, are performed on the event in step  1508 . Analytic utility detection operations are then performed on the resulting enriched event in step  1510  to identify entity behavior of analytic utility, as likewise described in greater detail herein. A determination is then made in step  1512  to determine whether the enriched event is associated with entity behavior of analytic utility. If not, then the process is continued, proceeding with step  1540 . Otherwise, an observable is derived from the event in step  1514 , as described in greater detail herein. 
     The resulting observable is then processed with associated observables in step  1516  to generate a security related activity, likewise described in greater detail herein. In turn, the resulting security related activity is processed with associated security related activities in step  1518  to generate an activity session, described in greater detail. Thereafter, as described in greater detail herein, the resulting activity session is processed in step  1520  to generate a corresponding activity session. In turn, the resulting activity session is processed with the activity session in step  1522  to generate an EBP element, which is then added to an associated EBP in step  1524 . 
     Thereafter, in step  1526 , certain contextualization information stored in a repository of EBC data may be retrieved and then used in step  1528  to perform entity behavior contextualization operations to generate inferences related to the entity&#39;s behavior. The EBP is then processed with resulting entity behavior inferences in step  1530  to associate the EBP with one or more corresponding risk use cases, as described in greater detail herein. In turn, the one or more risk use cases are then associated in step  1532  with one or more corresponding security vulnerability scenarios, as likewise described in greater detail herein. 
     Then, in step  1534 , entity behavior meaning derivation operations are performed on the EBP and each security vulnerability behavior scenario selected in step  1532  to determine whether the entity&#39;s behavior warrants performance of a security operation. Once that determination is made, a subsequent determination is made in step  1536  whether to perform a security operation. If not, then the process is continued, proceeding with step  1540 . Otherwise, the appropriate security operation, described in greater detail herein, is performed in step  1538  and the process is continued, proceeding with step  1540 . 
       FIG. 16  shows a functional block diagram of the operation of an entity behavior catalog (EBC) system implemented in accordance with an embodiment of the invention. In various embodiments, certain EBC-related information, described in greater detail herein, may be provided by various EBC data sources  810 , likewise described in greater detail herein. In certain embodiments, the EBC data sources  810  may include endpoint devices  304 , edge devices  202 , third party sources  1606 , and other  1620  data sources. In certain embodiments, the receipt of EBC-related information provided by third party sources  1606  may be facilitated through the implementation of one or more Apache NiFi connectors  1608 , familiar to skilled practitioners of the art. 
     In certain embodiments, activity sessionization and session fingerprint generation  1620  operations may be performed on the EBC-related information provided by the EBC data sources  810  to generate discrete sessions. As used herein, activity sessionization broadly refers to the act of turning event-based data into activity sessions, described in greater detail herein. In these embodiments, the method by which certain EBC-related information is selected to be used in the generation of a particular activity session, and the method by which the activity session is generated, is a matter of design choice. As likewise used herein, an activity session broadly refers to an interval of time during which one or more user or non-user behaviors are respectively enacted by a user or non-user entity. 
     In certain embodiments, the user or non-user behaviors enacted during an activity session may be respectively associated with one or more events, described in greater detail herein. In certain embodiments, an activity session may be implemented to determine whether or not user or non-user behaviors enacted during the session are of analytic utility. As an example, certain user or non-user behaviors enacted during a particular activity session may indicate the behaviors were enacted by an impostor. As another example, certain user or non-user behaviors enacted during a particular activity session may be performed by an authenticated entity, but the behaviors may be unexpected or out of the norm. 
     In certain embodiments, two or more activity sessions may be contiguous. In certain embodiments, two or more activity sessions may be noncontiguous, but associated. In certain embodiments, an activity session may be associated with two or more other activity sessions. In certain embodiments, an activity session may be a subset of another activity session. In certain embodiments, the interval of time corresponding to a first activity session may overlap an interval of time corresponding to a second activity session. In certain embodiments, an activity session may be associated with two or more other activity sessions whose associated intervals of time may overlap one another. Skilled practitioners of the art will recognize that many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope or intent of the invention. 
     The resulting activity sessions and session fingerprints are then ingested  1616 , followed by the performance of data enrichment  1614  operations familiar to those of skill in the art. In certain embodiments, user identifier information (ID) information provided by a user ID management system  1612  may be used to perform the data enrichment  1614  operations. In various embodiments, certain contextual information related to a particular entity behavior or event may be used to perform the data enrichment  1614  operations. In various embodiments, certain temporal information, such as timestamp information, related to a particular entity behavior or event may be used to perform the data enrichment  1614  operations. In certain embodiments, a repository of EBC data  690  may be implemented to include repositories of entity attribute data  694 , entity behavior data  695 , and behavioral model data  696 . In various embodiments, certain information stored in the repository of entity attribute data  694  may be used to perform the data enrichment operations  1614 . 
     In certain embodiments, the resulting enriched sessions may be stored in the repository of entity behavior data  695 . In certain embodiments, the resulting enriched sessions may be provided to a risk services  422  module, described in greater detail herein. In certain embodiments, as likewise described in greater detail herein, the risk services  422  module may be implemented to generate inferences, risk models, and risk scores, or a combination thereof. In certain embodiments, the resulting inferences, risk models, and risk scores, or a combination thereof, may then be stored in the repository of entity behavioral model data  696 . 
     In certain embodiments, the risk services  422  module may be implemented to provide input data associated with the inferences, risk models, and risk scores it may generate to a security policy service  1628 . In certain embodiments, the security policy service  1628  may be implemented to use the inferences, risk models, and risk scores to generate security policies. In turn, the security policy service  1628  may be implemented in certain embodiments to export  1630  the resulting security policies to endpoint agents or devices  304 , edge devices  202 , or other security mechanisms, where they may be used to limit risk, as described in greater detail herein. In certain embodiments, an EBC access module  122  may be implemented to provide administrative access to various components of the EBC system  120 , as shown in  FIG. 16 . In certain embodiments, the EBC access management module  122  may include a user interface (UI), or a front-end, or both, familiar to skilled practitioners of the art. 
       FIGS. 17 a  and 17 b    are a simplified block diagram showing reference architecture components of an entity behavior catalog (EBC) system implemented in accordance with an embodiment of the invention for performing certain EBC operations. In various embodiments, the EBC system  120  may be implemented to generate, manage, store, or some combination thereof, information related to the behavior of an associated entity. In certain embodiments, the EBC system  120  may be implemented to provide an inventory of entity behaviors for use when performing a security operation, described in greater detail herein. In certain embodiments, the EBC system  120  may be implemented to include an EBC access management  122 , an EBP management  124 , a security vulnerability scenario management  126 , a security risk use case management  128 , an event enrichment  680 , an analytic utility detection  682 , an entity behavior contextualization  684 , an entity behavior meaning derivation  686  module, an entity data anonymization  688  and a repository of EBC data  690 , or a combination thereof. 
     In various embodiments, the EBC access management  122  module may be implemented to provide access to certain functionalities performed by the EBC system  120 . In various embodiments, the EBC access management  122  module may be implemented to perform certain data management operations. In certain embodiments, the data management operations may include storing, indexing, and retrieving data stored in the repository of EBC catalog data  690 . In various embodiments, the data management operations may include certain search functionalities familiar to skilled practitioners of the art. In certain embodiments the EBC access management  122  module may be implemented with a user interface (UI), or a front end, likewise familiar to those of skill in the art, to facilitate accessing various functionalities performed by the EBC system  120 . In various embodiments, the EBC access management  122 , EBP management  124 , security vulnerability scenario management  126 , security risk use case management  128 , event enrichment  680 , analytic utility detection  682 , entity behavior contextualization  684 , entity behavior meaning derivation  686 , and entity data anonymization  688  modules, and the repository of EBC data  690 , or a combination thereof, may be implemented to provide an EBC reference architecture for performing certain EBC operations, described in greater detail herein. 
     In certain embodiments, an entity behavior catalog (EBC) system  120  may be implemented to identify a security related activity, described in greater detail herein. In certain embodiments, the security related activity may be based upon an observable, likewise described in greater detail herein. In certain embodiments, the observable may include event information corresponding to electronically-observable behavior enacted by an entity. In certain embodiments, the event information corresponding to electronically-observable behavior enacted by an entity may be received from an electronic data source, such as the EBC data sources  810  shown in  FIGS. 6 a   ,  8 ,  16 , and  17   b.    
     In certain embodiments, as likewise described in greater detail herein, the EBC system  120  may be implemented to identify a particular event of analytic utility by analyzing an observable associated with a particular security related activity. In certain embodiments, the EBC system  120  may be implemented to generate entity behavior catalog data based upon an identified event of analytic utility. In certain embodiments, an observable  906  may be derived, as described in greater detail herein, from an identified event of analytic utility. In various embodiments, the EBC system  120  may be implemented to associate certain entity behavior data it may generate with a predetermined abstraction level, described in greater detail herein. 
     In various embodiments, the EBC system  120  may be implemented to use certain entity behavior catalog data, and an associated abstraction level, to generate a hierarchical set of entity behaviors, described in greater detail herein. In certain embodiments, the hierarchical set of entity behaviors generated by the EBC system  120  may represent an associated security risk, likewise described in greater detail herein. Likewise, as described in greater detail herein, the EBC system  120  may be implemented in certain embodiments to store the hierarchical set of entity behaviors within a repository of EBC data  690 . 
     In various embodiments, the EBC system  120  may be implemented to receive certain event information, described in greater detail herein, corresponding to an event associated with an entity interaction, likewise described in greater detail herein. In various embodiments, the event information may be generated by, received from, or a combination thereof, certain EBC data sources  810 . In certain embodiments, such EBC data sources  810  may include endpoint devices, edge devices  202 , identity and access  1704  systems familiar to those of skill in the art, as well as various software and data security  1706  applications. In various embodiments, EBC data sources  810  may likewise include output from certain processes  1708 , network  1710  access and traffic logs, domain  1712  registrations and associated entities, certain resources  850 , described in greater detail herein, event logs  1714  of all kinds, and so forth. 
     In certain embodiments, EBC system  120  operations are begun with the receipt of information associated with a particular event. In certain embodiments, information associated with the event may include user entity profile attributes, user behavior factors, user entity mindset factors, entity state information, and contextual information, described in greater detail herein, or a combination thereof. In certain embodiments, the event may be associated with a user/device, a user/network, a user/resource, or a user/user interaction, as described in greater detail herein. In various embodiments, certain user entity profile data, user entity mindset profile data, non-user entity profile data, entity state data, contextual information, and temporal information stored in the repository of EBC data  690  may be retrieved and then used to perform event enrichment operations to enrich the information associated with the event. In certain embodiments, the event enrichment operations may be performed by the event enrichment  680  module. 
     In certain embodiments, analytic utility detection operations, described in greater detail herein, may be performed on the resulting enriched event to determine whether it is of analytic utility. In certain embodiments, the analytic utility detection operations may be performed by the analytic utility detection module  682 . In various embodiments, certain contextualization information stored in the repository of EBC data  690  may be retrieved and then used to perform entity behavior contextualization operations, likewise described in greater detail herein, to provide context, based upon the entity&#39;s user entity profile, or non-user entity profile, and its associated entity state. In certain embodiments, the entity behavior contextualization operations may be performed by the entity behavior contextualization module  684 . 
     In various embodiments, an observable  906 , described in greater detail herein, may be derived from the resulting enriched, contextualized event. As shown in  FIG. 17 b   , examples of such observables may include firewall file download  1718 , data loss protection (DLP) download  1720 , and various operating system (OS) events  1722 ,  1726 , and  1734 . As likewise shown in  FIG. 17 b   , other examples of such observables may include cloud access security broker (CASB) events  1724  and  1732 , endpoint spawn  1728 , insider threat process start  1730 , DLP share  1736 , and so forth. In certain embodiments, the resulting observables  906  may in turn be respectively associated with a corresponding observable abstraction level, described in greater detail herein. 
     In certain embodiments, security related activity abstraction operations, described in greater detail herein, may be performed on the resulting observables  906  to generate a corresponding security related activity  908 . In various embodiments, a security related activity  908  may be expressed in a Subject Action Object format and associated with observables  906  resulting from event information received from certain EBC data sources  810 . In certain embodiments, a security related activity abstraction operation, described in greater detail herein, may be performed to abstract away EBC data source-specific knowledge and details when expressing an entity behavior. For example, rather than providing the details associated with a “Windows:4624” non-user entity event, the security related activity  908  is abstracted to a “User Login To Device” OS event  1722 ,  1726 ,  1734 . 
     As shown in  FIG. 17 b   , examples of security related activities  908  may include “user downloaded document”  1722 , “device spawned process”  1744 , “user shared folder”  1746 , and so forth. To provide other examples, the security related activity  908  “user downloaded document”  1722  may be associated with observables  906  firewall file download  1718 , DLP download  1720 , OS event  1722 , and CASB event  1724 . Likewise, the security related activity  908  “device spawned process”  1744 , may be associated with observables  906 , OS event  1726 , endpoint spawn  1728 , and insider threat process start  1730 . The security related activity  908  “user shared folder”  1746  may likewise be associated with observables  906  CASB event  1732 , OS event  1734 , and DLP share  1736 . 
     In certain embodiments, security related activities  908  may in turn be respectively associated with a corresponding security related activity abstraction level, described in greater detail herein. In various embodiments, activity sessionization operations, likewise described in greater detail herein, may be performed to respectively associate certain events and security related activities  908  with corresponding activity sessions, likewise described in greater detail herein. Likewise, as described in greater detail herein, the resulting session information may be used in various embodiments to associate certain events of analytic utility, or their corresponding observables  906 , or their corresponding security related activities  908 , or a combination thereof, with a particular activity session. 
     In certain embodiments, the resulting security related activities  908  may be processed to generate an associated EBP element  980 , as described in greater detail herein. In various embodiments, the EBP element  980  may include user entity attribute  1748  information, non-user entity attribute  1750  information, entity behavior  1752  information, and so forth. In certain of these embodiments, the actual information included in a particular EBP element  980 , the method by which it is selected, and the method by which it is associated with the EBP element  980 , is a matter of design choice. In certain embodiments, the generation of a particular EBP element  980 , and its subsequent management, may be performed by the EBP management  124  module. In certain embodiments, the EBP elements  980  may in turn be respectively associated with a corresponding EBP element abstraction level, described in greater detail herein. 
     In various embodiments, certain EBP elements  980  may in turn be associated with a particular EBP  638 . In certain embodiments, the EBP  638  may be implemented as a class of user  1762  EBPs, an entity-specific user  1762  EBP, a class of non-user  1766  EBPs, an entity-specific non-user  1768  EBP, and so forth. In certain embodiments, classes of user  1762  and non-user  1766  EBPs may respectively be implemented as a prepopulated EBP, described in greater detail herein. 
     In certain embodiments, the association of EBP elements  980  with a particular EBP  638  may be performed by the EBP management  124  module. In various embodiments, certain entity data associated with EBP elements  980  associated with the classes of user  1762  and non-user  1766  EBPs may be anonymized. In certain embodiments, the anonymization of the entity data may be performed by the entity data anonymization  688  module. In certain embodiments, the EBP  638  may in turn be associated with an EBP abstraction level, described in greater detail herein. 
     In certain embodiments, security risk use case association operations may be performed to associate an EBP  638  with a particular security risk use case  1770 . As shown in  FIG. 17 a   , examples of such security risk use cases  1770  include “data exfiltration”  1772 , “data stockpiling”  1774 , “compromised insider”  1776 , “malicious user”  1778 , and so forth. In various embodiments, entity behavior of analytic utility resulting from the performance of certain analytic utility detection operations may be used identify one or more security risk use cases  1770  associated with a particular EBP  638 . In certain embodiments, identified security risk use cases may in turn be associated with a security risk use case abstraction level, described in greater detail herein. In certain embodiments, the security risk use case association, and security risk use case abstraction level association, operations may be performed by the security risk use case management  128  module. 
     In certain embodiments, the results of the security risk use case association operations may be used to perform security vulnerability scenario association operations to associate one or more security risk use cases  1770  to one or more security vulnerability scenarios  1780 , described in greater detail herein. As shown in  FIG. 17 a   , examples of security vulnerability scenarios  1780  include “accidental disclosure”  1782 , “account takeover”  1784 , “theft of data”  1786 , “sabotage”  1788 , “regulatory compliance”  1790 , “fraud”  1792 , “espionage”  1794 , and so forth. To continue the example, the “theft of data”  1786  security vulnerability scenario may be associated with the “data exfiltration”  1772 , “data stockpiling”  1774 , “compromised insider”  1776 , “malicious user”  1778  security risk use cases  1770 . Likewise, the “sabotage”  1788  and “fraud”  1792  security vulnerability scenarios may be respectively associated with some other security risk case  1770 . In certain embodiments, the associated security vulnerability scenarios may in turn be associated with a security vulnerability scenario abstraction level, described in greater detail herein. In certain embodiments, the security vulnerability scenario association, and the security vulnerability abstraction level association, operations may be performed by the security vulnerability scenario management  126  module. 
       FIG. 18  is a simplified block diagram showing the mapping of entity behaviors to a risk use case scenario implemented in accordance with an embodiment of the invention. In certain embodiments, an entity behavior catalog (EBC) system  120  may be implemented, as described in greater detail herein, to receive event information from a plurality of EBC data sources  810 , which is then processed to determine whether a particular event is of analytic utility. In certain embodiments, the EBC system  120  may be implemented to derive observables  906  from identified events of analytic utility, as likewise described in greater detail herein. In certain embodiments, the EBC system  120  may be implemented, as described in greater detail herein, to associate related observables  906  with a particular security related activity  908 , which in turn is associated with a corresponding security risk use case  1770 . In various embodiments, certain contextual information may be used, as described in greater detail herein, to determine which security related activities  908  may be associated with which security risk use cases  1770 . 
     In certain embodiments, a single  1860  security related activity  908  may be associated with a particular security risk use case  1770 . For example, as shown in  FIG. 18 , event data may be received from a Unix/Linux® event log  1812  and a Windows® directory  1804 . In this example, certain event data respectively received from the Unix/Linux® event log  1812  and Windows® directory  1804  may be associated with an event of analytic utility, which results in the derivation of observables  906  “File In Log Deleted”  1822  and “Directory Accessed”  1824 . To continue the example, the resulting observables  906  “File In Log Deleted”  1822  and “Directory Accessed”  1824  may then be associated with the security related activity  908  “Event Log Cleared”  1844 . In turn, the security related activity  908  “Event Log Cleared”  1844  may be associated with security risk use case  1770  “Administrative Evasion”  1858 . 
     In certain embodiments, two or more  1864  security related activities  908  may be associated with a particular security risk use case  1770 . For example, as shown in  FIG. 18 , event data may be received from an operating system (OS)  1806 , an insider threat  1808  detection system, an endpoint  1810  and a firewall  1812 . In this example, certain event data respectively received from the operating system (OS)  1806 , an insider threat  1808  detection system, an endpoint  1810  and a firewall  1812  may be associated with an event of analytic utility. Accordingly, observables  906  “Security Event ID”  1826 , “New Connection”  1826 , may be respectively derived from the event data of analytical utility received from the endpoint  1810  and the firewall  1812  EBC data sources  810 . Likewise, observables  906  “Connection Established”  1830  and “Network Scan”  1832  may be respectively derived from the event data of analytical utility received from the OS  1806 , the insider threat  1808  detection system, EBC data sources  810 . 
     To continue the example, the resulting observables  906  “Security Event ID”  1826 , “New Connection”  1826  and “Connection Established”  1830  may be associated with security related activity  908  “Device Connected To Port”  1846 . Likewise, observable  906  “Network Scan”  1832  may be associated with security related activity  908  “Network Scan”  1848 . In turn, the security related activities  908  “Device Connected To Port”  1846  and “Network Scan”  1832  may be associated with security risk use case  1770  “Internal Horizontal Scanning”  1862 . 
     In certain embodiments, a complex set  1868  of security related activities  908  may be associated with a particular security risk use case  1770 . For example, as shown in  FIG. 18 , event data may be received from an OS  1814 , an internal cloud access security broker (CASB)  1816 , an external CASB  1818 , and an endpoint  1820 . In this example, certain event data respectively received from the OS  1814 , the internal cloud access security broker (CASB)  1816 , the external CASB  1818 , and the endpoint  1820  may be associated with an event of analytic utility. 
     Accordingly, observables  906  “OS Event”  1834 , “CASB Event”  1840 , and “New Application”  1842  may be respectively derived from the event data of analytical utility provided by the OS  1814 , the external CASB  1818 , and the endpoint  1820  EBC data sources  810 . Likewise, a first “CASB Event ID”  1836  observable  906  and a second “CASB Event ID”  1838  observable  906  may both be derived from the event data of analytical utility received from the internal CASB  1816  EBC data source  810   
     To continue the example, the “OS Event”  1834 , the first “CASB Event ID”  1836 , and “New Application”  1842  observables  906  may then be respectively associated with security related activities  908  “New USB Device”  1850 , “Private Shareable Link”  1852 , and “File Transfer Application”  1856 . Likewise, second “CASB Event ID”  1838  observable  906  and the “CASB Event”  1840  observable  906  may then be associated with security related activity  908  “Public Shareable Link  1854 ”  1848 . In turn, the security related activities  908  “New USB Device”  1850 , “Private Shareable Link”  1852 , “Public Shareable Link  1854 ”  1848 , and “File Transfer Application”  1856  may be associated with security risk use case  1770  “Data Exfiltration Preparations”  1866 . 
       FIG. 19  is a simplified block diagram of an entity behavior catalog (EBC) system environment implemented in accordance with an embodiment of the invention. In certain embodiments, the EBC system environment may be implemented to detect user or non-user entity behavior of analytic utility and respond to mitigate risk, as described in greater detail herein. In certain embodiments, the EBC system environment may be implemented to include a security analytics system  118 , likewise described in greater detail herein. In certain embodiments, the security analytics system  118  may be implemented to include an EBC system  120 . 
     In certain embodiments, the EBC system  120 , as described in greater detail herein, may be implemented to use entity behavior information to generate an entity behavior profile (EBP), likewise as described in greater detail herein. In certain embodiments, the security analytics system  118  may be implemented to use one or more session-based fingerprints to perform security analytics operations to detect such user or non-user entity behavior. In certain embodiments, the security analytics system  118  may be implemented to monitor entity behavior associated with a user entity, such as a user ‘A’  702  or user ‘B’  772 . In certain embodiments, the user or non-user entity behavior may be monitored during user/device  730 , user/network  742 , user/resource  748 , and user/user  770  interactions. In certain embodiments, the user/user  770  interactions may occur between a first user, such as user ‘A’  702  and user ‘B’  772 . 
     In certain embodiments, as described in greater detail herein, an endpoint agent  306  may be implemented on an endpoint device  304  to perform user or non-user entity behavior monitoring. In certain embodiments, the user or non-user entity behavior may be monitored by the endpoint agent  306  during user/device  730  interactions between a user entity, such as user ‘A’  702 , and an endpoint device  304 . In certain embodiments, the user or non-user entity behavior may be monitored by the endpoint agent  306  during user/network  742  interactions between user ‘A’  702  and a network, such as an internal  744  or external  746  network. In certain embodiments, the user or non-user entity behavior may be monitored by the endpoint agent  306  during user/resource  748  interactions between user ‘A’  702  and a resource  750 , such as a facility, printer, surveillance camera, system, datastore, service, and so forth. In certain embodiments, the monitoring of user or non-user entity behavior by the endpoint agent  306  may include the monitoring of electronically-observable actions respectively enacted by a particular user or non-user entity. In certain embodiments, the endpoint agent  306  may be implemented in combination with the security analytics system  118  and the EBC system  120  to detect entity behavior of analytic utility and perform a security operation to mitigate risk. 
     In certain embodiments, the endpoint agent  306  may be implemented to include an event analytics  310  module and an EBP feature pack  2008 . In certain embodiments, the EBP feature pack  1908  may be further implemented to include an event data detector  1910  module, an entity behavior data detector  1912  module, an event and entity behavior data collection  1914  module, an analytic utility detection  1916  module, an observable derivation  1918  module, and a security related activity abstraction  1920  module, or a combination thereof. In certain embodiments, the event data detector  1910  module may be implemented to detect event data, described in greater detail herein, resulting from user/device  730 , user/network  742 , user/resource  748 , and user/user  770  interactions. In various embodiments, the entity behavior detector  2012  module may be implemented to detect certain user and non-user entity behaviors, described in greater detail herein, resulting from user/device  730 , user/network  742 , user/resource  748 , and user/user  770  interactions. 
     In various embodiments, the event and entity behavior data collection  1914  module may be implemented to collect certain event and entity behavior data associated with the user/device  730 , user/network  742 , user/resource  748 , and user/user  770  interactions. In certain embodiments, the analytic utility detection  1916  may be implemented to detect entity behavior of analytic utility associated with events corresponding to the user/device  730 , user/network  742 , user/resource  748 , and user/user  770  interactions. In various embodiments, the observable derivation  2018  module may be implemented to derive observables, described in greater detail herein, associated with events of analytical utility corresponding to the user/device  730 , user/network  742 , user/resource  748 , and user/user  770  interactions. In various embodiments, the security related activity abstraction  1918  module may be implemented to generate a security related activity, likewise described in greater detail herein, from the observables derived by the observable derivation  1916  module. 
     In certain embodiments, the endpoint agent  306  may be implemented to communicate the event and entity behavior collected by the event and entity behavior data collector  1914  module, the observables derived by the observable derivation  1916  module, and the security related activities generated by the security related activity abstraction  1918 , or a combination thereof, to the security analytics  118  system. In certain embodiments, the security analytics system  118  may be implemented to receive the event and entity behavior data, the observables, and the security related activities provided by the endpoint agent  306 . In certain embodiments, the security analytics system  118  may be implemented to provide the event and entity behavior data, the observables, and the security related activities, or a combination thereof, to the security analytics system  118 . In turn, in certain embodiments, the security analytics system  118  may be implemented in certain embodiments to provide the event and entity behavior data, the observables, and the security related activities, or a combination thereof, to the EBC system  120  for processing. 
     In certain embodiment, the EBC system  120  may be implemented to include an entity behavior contextualization  684  module, an EBP session generator  1982  module, an EBP element generator  1984 , or a combination thereof. In certain embodiments, the EBC system  120  may likewise be implemented to include an EBP management  124 , a security risk use case management  128  module, and a security vulnerability scenario management  126  module, or a combination thereof. In certain embodiments, the EBP element generator  1982  module may be implemented to process the event and entity behavior data, the observables, and the security related activities provided by the endpoint agent  306  to generate EBP elements, described in greater detail herein. In certain embodiments, the EBP session generator  1984  may be implemented to use the event and entity behavior data, the observables, and the security related activities provided by the endpoint agent  306 , to generate session information. In certain embodiments, the EBP session generator  1984  may be implemented to use the resulting session information to generate an activity session, described in greater detail herein. 
     In various embodiments, the EBP management  124  module may be implemented, as described in greater detail herein, to perform certain EBP management operations, described in greater detail herein. As likewise described in greater detail herein, certain EBP management operations may be performed to associate EBP elements generated by the EBP element generator  1982  module with a corresponding EBP. Likewise, certain EBP management operations may be performed to use the session information generated by the EBP session generator  1984  module to associate a particular EBP element with a particular EBP. In various embodiments, the security risk use case management  128  module may be implemented to perform certain security risk use case association operations, as described in greater detail herein. Likewise, as described in greater detail herein, the security vulnerability management  126  module may be implemented in various embodiments to perform certain security vulnerability scenario inference operations. 
     In certain embodiments, the EBC system  120  may be implemented as a distributed system. Accordingly, various embodiments of the invention reflect an appreciation that certain modules, or associated functionalities, may be implemented either within the EBC system  120  itself, the EBP feature pack  1908 , an edge device  202 , an internal  744  or external  746  network, an external system  780 , or some combination thereof. As an example, the functionality provided, and operations performed, by the analytic utility detection  1916 , observable derivation  2018  and security related activity abstraction  1920  modules may be implemented within the EBC system  120  in certain embodiments. Likewise, the functionality provided, and operations performed, by the entity behavior contextualization  684 , EBP session generator  1982 , and EBP element generator  1984  may be implemented within the EBP feature pack  1908 . Those of skill in the art will recognize that many such implementations are possible. Accordingly, the foregoing is not intended to limit the spirit, scope, or intent of the invention. 
       FIG. 20  is a simplified block diagram of an entity behavior catalog (EBC) system implemented in accordance with an embodiment of the invention to modify an existing, or generate a new, entity behavior profile (EBP). Certain aspects of the invention reflect an appreciation that the block diagram shown in this figure provides additional detail in support of the flowchart steps shown in  FIGS. 21 a - c    and  FIGS. 22 a - d   . In certain embodiments, as described in greater detail herein, a security analytics system  118  may be implemented to include an EBC system  120 . In certain embodiments, the EBC system  120  may be implemented to include an EBP management  124  module, or an entity data anonymization  688  module, or both. 
     In certain embodiments, the EBC system  120  may be implemented to receive a request  2002  to modify an existing, or generate a new, EBP  638 . In various embodiments, the request  2002  may be received by the EBP management  124  module, which then performs certain EBP management operations to generate a modified  2006  EBP or an entity-specific  2008  EBP. As used herein, a modified  2006  EBP broadly refers to an existing EBP  638  associated with a particular entity  2004  whose entity behavior information collection parameters have been revised. In certain embodiments, the revisions to the EBP&#39;s  638  entity behavior information collection parameters may include increasing, decreasing, or otherwise changing the amount, type, or nature of entity behavior information that is collected. 
     As an example, a user entity  2004  may be employed as a claims processor for an insurance company and have an associated EBP  638  containing information related to their identity and behavior in that role. In this example, the user entity  2004  may accept a new position as a claims adjustor, which in turn may involve accessing certain additional information resources. As a result, the user entity&#39;s  2004  associated EBP  638  may be processed by the EBP management  124  module to generate a modified EBP  2006 , which in turn is then processed to contain certain EBP elements associated with the new information resources. Once the modified EBP  2006  is generated, it can then be associated with the user entity  2004  as their current EBP  638 . 
     In various embodiments, the EBP&#39;s  638  information collection parameters may be associated with certain EBP elements, described in greater detail herein. In certain of these embodiments, the information collected by, or associated with, a particular EBP  638 , whether directly or indirectly, may be related to its associated entity&#39;s  2004  identity and entity behavior, as likewise described in greater detail herein. In certain embodiments, a modified EBP  2006  generated by the EBP management  124  module from an existing EBP  638  associated with a particular entity  2004  may be implemented to replace the entity&#39;s  2004  existing EBP  638 . 
     In certain embodiments, a modified EBP  2006  generated by the EBP management  124  module from an existing EBP  638  associated with a particular entity  2004  may be implemented to be associated with the same entity  2004  in addition to its existing EBP  638 . As an example, a user entity  2004  may have a primary role in an organization, and as a result have a primary EBP  638 . However, the same user entity  2004  may be assigned a secondary role. As a result, the user entity&#39;s  2004  existing EBP  638  may be processed by the EBP management  124  module to generate a modified EBP  2006 , which in turn is revised to accommodate entity behavior information related to the user entity&#39;s  2004  secondary role. The resulting modified EBP  2006  can then be associated with the user entity  2004  as a second EBP  638 . 
     In certain embodiments, a modified EBP  2006  generated by the EBP management  124  module from a particular EBP  638  associated with a first entity  2004  may be implemented to be associated with a second entity  2004 . For example, a user entity  2004  may be employed by a company as an account executive. As a result, information related to their associated behavior may be stored in an associated EBP  638 . In this example, another user entity  2004  may be hired as a second account executive. To continue the example, the first user entity&#39;s  2004  existing EBP  638  may be processed by the EBP management  124  module to generate a modified EBP  2006 , which in turn can then be associated with the second user entity  2004  as their initial EBP  638 . 
     In various embodiments, a prepopulated EBP  678  may be implemented to contain certain prepopulated EBP elements that have been anonymized. In certain of these embodiments, the entity data anonymization  688  module may be implemented to perform the anonymization of the prepopulated EBP elements. In various embodiments, a modified EBP  2006  generated by the EBP management  124  module from a particular EBP  638  associated with a first entity  2004  may be implemented to contain certain EBP elements that have been anonymized prior to being associated with a second entity  2004 . To continue the prior example, any personally-identifiable information (PII) associated with the first user entity  2004  in the modified EBP  2006  is anonymized by the entity data anonymization  688  module. Once all PII has been anonymized, the resulting modified EBP  2006  can then be associated with the second user entity  2004  as a their initial EBP  638 . 
     As likewise used herein, an entity-specific  2008  EBP broadly refers to an EBP  638  that may contain certain EBP elements related to a particular entity  2004 , but not yet associated with the entity  2004 . For example, a user entity  2004  may join an organization, and as a result, provide certain PII, which in turn may be processed with certain entity attribute data to generate user credentials, authentication factors, access rights and so forth. In this example the resulting EBP elements may then be used to populate an entity-specific EBP  2006 , which in turn may then be associated with the user entity  2004  as their initial EBP  638 . In this example, information related to the user entity&#39;s behavior is then added to their EBP  638  over time. 
     In various embodiments, an entity-specific  2008  EBP implemented as an EBP  638  may be associated with a user entity  2004  whose identity may not be initially known. As an example, an unknown user entity  2004  may begin navigating the web site of an organization. In this embodiment, an entity-specific  2008  may be implemented as an EBP  638 , which is then associated with the unknown user entity  2004 . From that point forward, the unknown user entity&#39;s  2004  behavior is captured and stored in its associated EBP  638 . 
     In certain of these embodiments, the identity of an entity  2004  associated with an EBP  638  may be established over time. To continue the prior example, at some point in time, the unknown user entity  2004  may be requested to provide some form of identity information. If there is a positive response to the request, then the identity information is added to the EBP  638  associated with the user entity  2004 . 
     In certain embodiments, the EBP management  124  module may be implemented to generate a prepopulated EBP  678 , described in greater detail herein. In various embodiments, the EBP generator  124  module may be implemented to process one or more prepopulated EBPs  678  previously-generated by the EBP management  124  module to in turn generate an EBP  638 . In certain of these embodiments, the resulting EBP  638  may then be associated with one or more entities  2004 . In these embodiments, the method by which the entity  2004  is selected, and the resulting EBP  638  is associated with it, is a matter of design choice. 
     In certain embodiments, a prepopulated EBP  678  may be implemented as a universal  2010 , a segmented  2012 , an entity-pertinent  2014 , or entity-specific  2016  prepopulated EBP. As used herein, a universal  2010  prepopulated EBP broadly refers to a prepopulated EBP  678  implemented to accommodate certain prepopulated EBP elements, as described in greater detail herein. In certain embodiments, the universal  2010  prepopulated EBP may be implemented to accommodate prepopulated EBP elements corresponding to EBP elements supported by a particular EBP  638 . 
     As an example, an EBP  638  may be implemented in certain embodiments to include EBP elements associated with a user entity profile, a user entity mindset profile, a non-user entity profile, and an entity state, as described in greater detail herein. Accordingly, a corresponding universal  2010  prepopulated EBP may be implemented in certain embodiments to include prepopulated EBP elements associated with a prepopulated user entity profile, a prepopulated user entity mindset profile, a prepopulated non-user entity profile, and a prepopulated entity state, as likewise described in greater detail herein. 
     In certain embodiments, prepopulated EBP elements associated with a prepopulated user entity profile may include prepopulated user profile attributes, prepopulated user behavior attributes, and prepopulated user mindset factors, as likewise describe in greater detail herein. In various embodiments, prepopulated EBP elements associated with a prepopulated non-user entity profile may likewise include certain prepopulated non-user profile attributes, as described in greater detail herein. In these embodiments, the prepopulated EBP elements selected for inclusion in a universal  2010  prepopulated EBP, and the method by which they are selected, is a matter of design choice. 
     As used herein, a segmented  2012  prepopulated EBP broadly refers to a prepopulated EBP  678  implemented to be composed of distinct groups, or segments, of prepopulated EBP elements. In certain embodiments, one or more segments of a segmented  2012  prepopulated EBP may be used, individually or in combination, by the EBP management  124  module to generate an EBP  638 . In various embodiments, the prepopulated EBP elements selected for use as segment of a segmented  2012  EBP, and the method by which they are selected, is a matter of design choice. 
     As an example, in certain embodiments, the segments of a prepopulated EBP  678  selected for use as a segmented  2012  prepopulated EBP may include prepopulated EBP elements associated with a prepopulated user entity profile, a prepopulated user entity mindset profile, a prepopulated non-user entity profile, or a prepopulated entity state, or a combination thereof. As another example, in certain embodiments, the segments of a prepopulated EBP  678  selected for use as a segmented  2012  prepopulated EBP may include prepopulated EBP elements associated with prepopulated user profile attributes, prepopulated user behavior attributes, prepopulated user mindset factors, and prepopulated non-user profile attributes. Those of skill in the art will recognize many such embodiments are possible. Accordingly, the foregoing is not intended to limit the spirit, scope, or intent of the invention. 
     As used herein, an entity-pertinent  2014  prepopulated EBP broadly refers to a prepopulated EBP  678  that contains one or more prepopulated EBP elements commonly associated with a particular type or class of entity  2004 . As an example, a cloud service provider may currently have three servers of the same manufacturer and configuration providing email service. Over time, the number of email accounts being serviced may grow and the capacity of the three current servers may begin to surpass their optimum operational parameters. As a result, the service provider may decide to purchase and deploy two additional servers from the same manufacturer with the same configuration to maintain optimum service levels. 
     In this example, all five servers are from the same manufacturer, have the same configuration, and are used for the same purpose. As a result, they can be considered a class of non-user entities, described in greater detail herein. To continue the example, the three original servers may each have an associated EBP  638 . In further continuance of this example, certain EBP elements contained in the original EBPs  638  may be processed by the EBP management  124  module to generate a normalized set of EBP elements. In this example, the resulting set of normalized EBP elements can then be used as prepopulated EBP elements, likewise described in greater detail herein, by the EBP management  124  module to generate an entity-pertinent  2014  prepopulated EBP. In turn, the resulting entity-pertinent  2014  prepopulated EBP can be associated with the two new servers as their respective EBPs  638 . 
     As used herein, an entity-specific  2016  prepopulated EBP broadly refers to a prepopulated EBP  678  that contains one or more prepopulated EBP elements associated with a particular entity  2004 . As an example, a company may have a vice president of sales, a senior sales executive, and an inside sales associate, all of which have an associated EBP  638  reflecting their associated user entity&#39;s identity, access rights, and behavior. In this example, the company decides to hire an additional inside sales associate. Since there is only one other inside sales associate, their current EBP  638  may be retrieved and processed to generate an entity-specific  2016  prepopulated EBP. In certain embodiments, the entity data anonymization  688  module may be implemented to anonymize PII in the resulting entity-specific  2016  prepopulated EBP. Once any PII the entity-specific  2016  prepopulated EBP may contain has been anonymized, it can be associated with the newly-hired inside sales associate as their EBP  638 . 
     In certain embodiments, the EBP management  124  module may be implemented to process a particular modified  2010  EBP for conversion into a prepopulated  678  EBP. In various embodiments, the EBP management  124  module may be implemented to use the entity data anonymization  688  module when converting a modified EBP  2010  into a prepopulated EBP  678  to anonymize certain entity behavior profile  693  entity attribute  694 , entity behavior  695 , or entity behavior model  696  data associated with an entity  2004 . In certain embodiments, the EBP elements contained in a prepopulated EBP  678  may be implemented to contain, or reference, structured data, non-structured data, or a combination thereof. 
     In various embodiments, prepopulated EBP  678  modification and generation operations may be initiated by the receipt of a request  2002  to perform certain EBP operations to modify an existing, or generate a new, prepopulated EBP  678 . In certain of these embodiments, the request  2002  is processed by the EBP management  124  module to retrieve relevant EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data associated with one or more entities  2004 . If the request  2002  is to modify an existing prepopulated EBP  678 , then the request  2002  may be processed in certain embodiments with the retrieved entity  692  and EBP  690  data, and the existing prepopulated EBP  678 , to determine which modifications are to be made. If it is decided to use one or more other prepopulated EBPs  678  relevant to making the modifications, then the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the existing prepopulated EBP  678 , is processed to identify other relevant prepopulated EBPs  678  that may be used to make the modifications. 
     Once identified, the identified prepopulated EBPs  678  are retrieved and then processed with the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the existing prepopulated EBP  678 , to generate a modified prepopulated EBP  678 . In certain embodiments, the modified prepopulated EBP  678  may be generated as a universal  2010 , a segmented  2012 , an entity-pertinent  2014 , or an entity-specific  2016  prepopulated EBP. The resulting prepopulated EBP  678  may then be stored in the repository of EBP data  693 . 
     In certain embodiments, it may be decided to not use one or more other prepopulated EBPs  678  relevant to making the modifications. If so, then the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the existing prepopulated EBP  678  are processed to generate a modified prepopulated EBP  678  as previously described. As likewise previously described, the resulting prepopulated EBP  678  may then be stored in the repository of EBP data  693 . 
     In various embodiments, it may be decided to use a universal  2010  EBP to generate a new prepopulated EBP  678  instead of modifying an existing prepopulated EBP  678 . In certain of these embodiments, the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and a target universal  2010  prepopulated EBP are then processed to generate a new prepopulated EBP  678 . As previously described, the resulting prepopulated EBP  678  may then be stored in the repository of EBP data  693 . 
     In various embodiments, it may be decided to use a segmented  2012  EBP to generate a new prepopulated EBP  678  instead of modifying an existing prepopulated EBP  678 . In certain of the embodiments, the request  2002 , along with relevant EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data is processed to identify relevant segments of one or more target segmented  2012  prepopulated EBPs. In turn, the request  2002 , the relevant EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the identified relevant EBP segments, are then processed to generate a new prepopulated EBP  678 . As previously described, the resulting prepopulated EBP  678  may then be stored in the repository of EBPs  693  data. 
     In various embodiments, it may be decided to use an entity-pertinent  2014  EBP to generate a new prepopulated EBP  678  instead of modifying an existing prepopulated EBP  678 . In certain of these embodiments, the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and a target entity-pertinent  2014  prepopulated EBP, are then processed to generate a new prepopulated EBP  678 . As previously described, the resulting prepopulated EBP  678  may then be stored in a repository of EBP  693  data. 
     In various embodiments, it may be decided to use an entity-specific  2016  EBP to generate a new prepopulated EBP  678  instead of modifying an existing prepopulated EBP  678 . In certain of these embodiments, the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and a target entity-specific  2016  prepopulated EBP, are then processed to generate a new prepopulated EBP  678 . As previously described, the resulting prepopulated EBP  678  may then be stored in a repository of EBP  693  data. 
     In various embodiments, EBP  638  modification and generation operations may be initiated by the receipt of a request  2002  to perform certain EBP management operations to modify an existing, or generate a new, prepopulated EBP  638  for a target entity  2004 . In certain of these embodiments, the request  2002  is processed to retrieve relevant EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data associated with one or more entities  2004 . 
     If the request  2002  is to modify an existing EBP  638 , then the request  2002  may be processed in certain embodiments with the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the existing EBP  638 , to determine which modifications are to be made. If it is decided to use one or more other EBPs  638  relevant to making the modifications, then the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the existing EBP  638 , is processed to identify other relevant EBPs  638  that may be used to make the modifications. In certain embodiments, the relevant EBPs  638  that may be used to make the modifications may be stored in the repository of EBP  693  data. 
     Once identified, the identified other EBPs  638  are retrieved and then processed with the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the existing EBP  638 , to generate a modified EBP  638 . The resulting modified EBP  638  may then be associated with the entity  2004 . In certain embodiments, it may be decided to not use one or more other EBPs  638  relevant to making the modifications. If so, then the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the existing EBP  638 , are processed to generate a modified EBP  2006  as previously described. As previously described, the resulting modified EBP  2006  may then be associated with the entity  2004 . 
     In various embodiments, it may be decided to use a universal  2010  EBP to generate a new EBP  638  instead of modifying an existing EBP  638 . In certain of these embodiments, the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and a target universal  2010  prepopulated EBP, are then processed to generate a new EBP  638 . As previously described, the resulting new EBP  638  may then be associated with the entity  2004 . 
     In various embodiments, it may be decided to use a segmented  2012  EBP to generate a new EBP  638  instead of modifying an existing EBP  638 . In certain embodiments, the request  2002 , along with relevant EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data is processed to identify relevant segments of one or more target segmented  2012  prepopulated EBPs. In turn, the request  2002 , the relevant EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and the identified relevant EBP segments, are then processed to generate a new EBP  638 . As previously described, the resulting new EBP  638  may then be associated with the entity  2004 . 
     In various embodiments, it may be decided to use an entity-pertinent  2014  EBP to generate a new EBP  638  instead of modifying an existing EBP  638 . In certain of these embodiments, the request  2002 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and a target entity-pertinent  2014  prepopulated EBP, are then processed to generate a new EBP  638 . As previously described, the resulting new EBP  638  may then be associated with the entity  2004 . 
     In various embodiments, it may be decided to use an entity-specific  2016  prepopulated EBP to generate a new EBP  638  instead of modifying an existing EBP  638 . In certain of these embodiments, the request  2008 , the retrieved EBP  693 , entity attribute  694 , entity behavior  695 , and entity behavior model  696  data, and a target entity-specific  2016  prepopulated EBP are then processed to generate a new EBP  638 . As previously described, the resulting new EBP  638  may then be associated with the entity  2004 . 
       FIGS. 21 a  through 21 c    are a generalized flowchart of entity behavior profile (EBP) system operations performed in accordance with an embodiment of the invention to generate a new, or modify an existing, ATP for an associated entity. In this embodiment, EBP modification and generation operations are begun in step  2102 , followed by the receipt of a request in step  2104  to perform certain EBP operations to modify an existing, or generate a new, EBP for a target entity. In turn, the request is processed in step  2106  to retrieve relevant entity and AP data associated with the entity, as described in greater detail herein. 
     A determination is then made in step  2108  whether the request received in step  2104  is to modify an existing EBP associated with the target entity. If so, then the request is processed in step  2110  with the retrieved entity and EBP data, and the existing EBP, to determine which modifications are to be made. A determination is then made in step  2112  whether to use one or more other prepopulated EBPs relevant to making the modifications. If so, the request, the retrieved entity and EBP data, and the existing EBP is processed in step  2114  to identify other relevant prepopulated EBPs that may be used to make the modifications. 
     Once identified, the identified prepopulated EBPs are retrieved in step  2216  and then processed in step  2218  with the request, the retrieved entity and EBP data, and the existing EBP, to generate a modified EBP. The resulting modified EBP is then associated with the entity in step  2148 , followed by a determination being made in step  2150  whether to continue operations to modify an existing, or generating a new, EBP. If not, then the process is continued, proceeding with step  2104 . Otherwise, operations to modify an existing, or generating a new, EBP are ended in step  2152 . 
     However, if it was determined in step  2108  not to modify an existing EBP associated with an entity, then a determination is made in step  2122  whether to use a non-prepopulated EBP to generate a new EBP for the target entity. If so, the request, the retrieved entity and EBP data, and a target non-prepopulated EBP are processed in step  2124  to generate a new EBP for the target entity. The process is then continued, proceeding with step  2148 . 
     However, if it was determined in step  2122  not to use a non-prepopulated EBP, then a determination is made in step  2126  whether to use a universal prepopulated EBP to generate a new EBP for the target entity. If so, the request, the retrieved entity and EBP data, and a target universal prepopulated EBP are processed in step  2128  to generate a new EBP for the target entity. The process is then continued, proceeding with step  2148 . 
     However, if it was determined in step  2126  not to use a universal prepopulated EBP, then a determination is made in step  2130  whether to use a segmented prepopulated EBP to generate a new EBP for the target entity. If so, the request, along with the retrieved entity and EBP data, is then processed in step  2132  to identify relevant segments of one or more segmented prepopulated EBPs. The request, the retrieved entity and EBP data, and the identified relevant EBP segments are then processed in step  2134  to generate a new EBP for the target entity. The process is then continued, proceeding with step  2148 . 
     However, if it was determined in step  2130  not to use a segmented prepopulated EBP, then a determination is made in step  2136  whether to use an entity-pertinent prepopulated EBP to generate a new EBP for the target entity. If so, the request, along with the retrieved entity and EBP data, is processed in step  2138  to identify one or more entity-pertinent prepopulated EBPs. The request, along with the retrieved entity and EBP data, and the one or more identified entity-pertinent prepopulated EBPs are processed in step  2140  to generate a new EBP for the target entity. The process is then continued, proceeding with step  2148 . 
     However, if it was determined in step  2136  not to use an entity-pertinent prepopulated EBP, then a determination is made in step  2142  whether to use an entity-specific prepopulated EBP to generate a new EBP for the target entity. If so, the request, along with the retrieved entity and EBP data, is processed in step  2144  to identify one or more entity-specific prepopulated EBPs. The request, along with the retrieved entity and EBP data, and the one or more identified entity-specific prepopulated EBPs are processed in step  2146  to generate a new EBP for the target entity. The process is then continued, proceeding with step  2148 . 
       FIGS. 22 a  through 22 d    are a generalized flowchart of entity behavior profile (EBP) system operations performed in accordance with an embodiment of the invention to generate a new, or modify an existing, prepopulated EBP. In this embodiment, prepopulated EBP modification and generation operations are begun in step  2202 , followed by the receipt of a request in step  2204  to perform certain EBP operations to modify an existing, or generate a new, prepopulated EBP. In turn, the request is processed in step  2206  to retrieve relevant entity and EBP data, as described in greater detail herein. 
     A determination is then made in step  2208  whether the request received in step  2204  is to modify an existing prepopulated EBP. If so, then the request is processed in step  2210  with the retrieved entity and EBP data, and the existing prepopulated EBP, to determine which modifications are to be made. A determination is then made in step  2212  whether to use one or more other prepopulated EBPs relevant to making the modifications. If so, the request, the retrieved entity and EBP data, and the existing prepopulated EBP is processed in step  2214  to identify other relevant prepopulated EBPs that may be used to make the modifications. 
     Once identified, the identified prepopulated EBPs are retrieved in step  2216  and then processed in step  2218  with the request, the retrieved entity and EBP data, and the existing prepopulated EBP, to generate a modified prepopulated EBP. The resulting prepopulated EBP is then stored in a repository of prepopulated EBPs in step  2246 , followed by a determination being made in step  2248  whether to continue operations to modify an existing, or generating a new, prepopulated EBP. If not, then the process is continued, proceeding with step  2204 . 
     Otherwise, operations to modify an existing, or generating a new, prepopulated EBP are ended in step  2250 . However, if it was determined in step  2212  not to use one or more other prepopulated EBPs relevant to making the modifications, then the request, the retrieved entity and EBP data, and the existing prepopulated EBP are processed in step  2220  to generate a modified prepopulated EBP. The process is then continued, proceeding with step  2246 . 
     However, if it was determined in step  2208  not to modify an existing prepopulated EBP, then a determination is made in step  2222  whether to use a universal prepopulated EBP to generate a new prepopulated EBP. If so, the request, the retrieved entity and EBP data, and a target universal prepopulated EBP are processed in step  2224  to generate a new prepopulated EBP. The process is then continued, proceeding with step  2246 . 
     However, if it was determined in step  2222  not to use a universal prepopulated EBP, then a determination is made in step  2226  whether to use a segmented prepopulated EBP to generate a new prepopulated EBP. If so, the request, along with the retrieved entity and EBP data, is then processed in step  2228  to determine relevant segments of one or more segmented prepopulated EBPs. The request, the retrieved entity and EBP data, and the identified relevant EBP segments are then processed in step  2232  to generate a new prepopulated EBP. The process is then continued, proceeding with step  2246 . 
     However, if it was determined in step  2226  not to use a segmented prepopulated EBP, then a determination is made in step  2234  whether to use an entity-pertinent prepopulated EBP to generate a new prepopulated EBP. If so, the request, along with the retrieved entity and EBP data, is processed in step  2236  to identify one or more entity-pertinent prepopulated EBPs. The request, along with the retrieved entity and EBP data, and the one or more identified entity-pertinent prepopulated EBPs are processed in step  2238  to generate a new EBP for the target entity. The process is then continued, proceeding with step  2246 . 
     However, if it was determined in step  2234  not to use an entity-pertinent prepopulated EBP, then a determination is made in step  2240  whether to use an entity-specific prepopulated EBP to generate a new prepopulated EBP. If so, the request, along with the retrieved entity and EBP data, is processed in step  2244  to identify one or more entity-specific prepopulated EBPs. The request, along with the retrieved entity and EBP data, and the one or more identified entity-specific EBPs are processed in step  2244  to generate a new prepopulated EBP. The process is then continued, proceeding with step  2246 . 
     As will be appreciated by one skilled in the art, the present invention may be embodied as a method, system, or computer program product. Accordingly, embodiments of the invention may be implemented entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in an embodiment combining software and hardware. These various embodiments may all generally be referred to herein as a “circuit,” “module,” or “system.” Furthermore, the present invention may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium. 
     Any suitable computer usable or computer readable medium may be utilized. The computer-usable or computer-readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, or a magnetic storage device. In the context of this document, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
     Computer program code for carrying out operations of the present invention may be written in an object oriented programming language such as Java, Smalltalk, C++or the like. However, the computer program code for carrying out operations of the present invention may also be written in conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Embodiments of the invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     While particular embodiments of the present invention have been shown and described, it will be obvious to those skilled in the art that, based upon the teachings herein, changes and modifications may be made without departing from this invention and its broader aspects. Therefore, the appended claims are to encompass within their scope all such changes and modifications as are within the true spirit and scope of this invention. Furthermore, it is to be understood that the invention is solely defined by the appended claims. It will be understood by those with skill in the art that if a specific number of an introduced claim element is intended, such intent will be explicitly recited in the claim, and in the absence of such recitation no such limitation is present. For non-limiting example, as an aid to understanding, the following appended claims contain usage of the introductory phrases “at least one” and “one or more” to introduce claim elements. However, the use of such phrases should not be construed to imply that the introduction of a claim element by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an”; the same holds true for the use in the claims of definite articles. 
     The present invention is well adapted to attain the advantages mentioned as well as others inherent therein. While the present invention has been depicted, described, and is defined by reference to particular embodiments of the invention, such references do not imply a limitation on the invention, and no such limitation is to be inferred. The invention is capable of considerable modification, alteration, and equivalents in form and function, as will occur to those ordinarily skilled in the pertinent arts. The depicted and described embodiments are examples only, and are not exhaustive of the scope of the invention. 
     Consequently, the invention is intended to be limited only by the spirit and scope of the appended claims, giving full cognizance to equivalents in all respects.