Patent Publication Number: US-2018048725-A1

Title: Determination of user reputation regarding data object exposure in cloud computing environments

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 14/461,736, entitled “DETERMINATION OF USER REPUTATION REGARDING DATA OBJECT EXPOSURE IN CLOUD COMPUTING ENVIRONMENTS,” filed on Aug. 18, 2014, and claims priority to U.S. Provisional Patent Application No. 62/022,818, entitled “DETERMINATION OF USER REPUTATION REGARDING DATA OBJECT EXPOSURE IN CLOUD COMPUTING ENVIRONMENTS,” filed on Jul. 10, 2014, and U.S. Provisional Patent Application No. 61/984,923, entitled “DETERMINATION OF DATA OBJECT EXPOSURE IN CLOUD COMPUTING ENVIRONMENTS,” filed on Apr. 28, 2014, which are both hereby incorporated by reference in their entirety. 
    
    
     TECHNICAL BACKGROUND 
     Cloud computing is becoming more prevalent. Some of the more basic cloud computing services store files so that the files can be accessed from any computer system that is able to log into the cloud computing service storing the files. More complex cloud computing services provide a remote platform for providing their services to customers. These more complex cloud computing services are sometimes referred to as Software as a Service (SaaS), Platform as a service (PaaS), and Infrastructure as a Service (IaaS). 
     While various cloud computing services provide services to individuals, some cloud computing services also provide services to entities. For example, a business entity may subscribe to a particular cloud computing service for use by employees of the business entity. Each of the employees may be able to independently modify access settings for data objects maintained in the cloud computing service. Consequently, the entity may not be aware when settings for the data objects provide more exposure for the object than would be desired by the entity. For example, an entity may prefer that settings for an object prevent access by people or applications outside of the entity because of sensitive information within the object but has no practicable means of enforcing such a preference. 
     Overview 
     Embodiments disclosed herein provide systems, methods, and computer readable media for determining user reputation regarding data object exposure in a cloud computing environment. In a particular embodiment, a method provides receiving, from the cloud computing environment, information regarding behavior of a user in the cloud computing environment. The method further provides analyzing the information to determine a plurality of exposure characteristics for the user. The method provides determining a reputation of the user for exposing data objects in the cloud computing environment based on the plurality of exposure characteristics. 
     In some embodiments, the method further provides that determining a reputation of the user for exposing data objects in the cloud computing environment, comprises applying a plurality of rules to the plurality of exposure characteristics and determining the reputation based on how well the plurality of exposure characteristics comply with the plurality of rules. 
     In some embodiments, the information includes accessibility settings for a plurality of data objects associated with the user in the cloud computing environment. 
     In some embodiments, the information includes classifications for each of a plurality of data objects associated with the user. 
     In some embodiments, the method provides that receiving the information comprises using Application Programming Interface (API) calls for the cloud computing environment to request the information. 
     In some embodiments, the method provides that using API calls for the cloud computing environment to request the information comprises impersonating a user associated with the data object, wherein the API calls comprise API calls only available to the user. 
     In some embodiments, the method further provides generating a notification indicating the reputation and remedial actions that will compensate for the reputation. 
     In some embodiments, the cloud computing environment includes at least two cloud computing services associated with the user and the method provides that receiving the information comprises receiving first information regarding behavior of the user from a first cloud computing service of the cloud computing services and receiving second information regarding behavior of the user from a second cloud computing service of the cloud computing services. 
     In some embodiments, the method provides that determining the reputation comprises lowering a current reputation if the plurality of exposure characteristics indicates a higher risk of exposure than a risk of exposure indicated by the current reputation. 
     In a further embodiment, a computer readable storage medium having instructions stored thereon is provided for operating a reputation analysis system to determine user reputation regarding data object exposure in a cloud computing environment. The instructions, when executed by the reputation analysis system, direct the reputation analysis system to receive, from the cloud computing environment, information regarding behavior of a user in the cloud computing environment. The instructions further direct the reputation analysis system to analyze the information to determine a plurality of exposure characteristics for the user and determine a reputation of the user for exposing data objects in the cloud computing environment based on the plurality of exposure characteristics. 
     In yet another embodiment, a reputation analysis system is provided for determining user reputation regarding data object exposure in a cloud computing environment. The reputation analysis system includes a network communication interface configured to receive, from the cloud computing environment, information regarding behavior of a user in the cloud computing environment. The reputation analysis system further includes a processing system configured to analyze the information to determine a plurality of exposure characteristics for the user and determining a reputation of the user for exposing data objects in the cloud computing environment based on the plurality of exposure characteristics. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a cloud computing system for determining user reputation regarding data object exposure in a cloud computing environment. 
         FIG. 2  illustrates an operation of the cloud computing system to determine user reputation regarding data object exposure in a cloud computing environment. 
         FIG. 3  illustrates a cloud computing system for determining user reputation regarding data object exposure in a cloud computing environment. 
         FIG. 4  illustrates an operation of the cloud computing system to determine user reputation regarding data object exposure in a cloud computing environment. 
         FIG. 5  illustrates an operation of the cloud computing system to determine user reputation regarding data object exposure in a cloud computing environment. 
         FIG. 6  illustrates a cloud computing service for determining user reputation regarding data object exposure in a cloud computing environment. 
         FIG. 7  illustrates a cloud computing service for determining user reputation regarding data object exposure in a cloud computing environment. 
         FIG. 8  illustrates data object characteristics for determining user reputation regarding data object exposure in a cloud computing environment. 
         FIG. 9  illustrates a reputation analysis system for determining user reputation regarding data object exposure in a cloud computing environment. 
     
    
    
     DETAILED DESCRIPTION 
     The following description and associated figures teach the best mode of the invention. For the purpose of teaching inventive principles, some conventional aspects of the best mode may be simplified or omitted. The following claims specify the scope of the invention. Note that some aspects of the best mode may not fall within the scope of the invention as specified by the claims. Thus, those skilled in the art will appreciate variations from the best mode that fall within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. As a result, the invention is not limited to the specific examples described below, but only by the claims and their equivalents. 
       FIG. 1  illustrates cloud computing system  100 . Cloud computing system  100  includes reputation analysis system  101 , cloud computing environment  102 , and user system  103 . Cloud computing environment  102  includes storage system  121 , which stores information  131 . Reputation analysis system  101  and cloud computing environment  102  communicate over communication link  110 . Cloud computing environment  102  and user system  103  communicate over communication link  111 . 
     In operation, cloud computing environment  102  provides one or more cloud computing services to user system  103 . While not shown, cloud computing environment  102  may also provide services to any number of user systems beyond user system  103 . The services provided by cloud computing environment  102  may include file storage and sharing, document collaboration, client and customer management services, cloud based operating system execution, or any other possible service that may be provided to a user via a remote computing platform. Example cloud services include BOX, DROPBOX, SALESFORCE, YAMMER, FACEBOOK, EVERNOTE, or any other SaaS, PaaS, or IaaS. 
     Information  131  is maintained in storage system  121  and regards behavior of a user of user system  103  in cloud computing environment  102 . Specifically, cloud environment  121  maintains data objects to provide services to user system  103  and its user. The data objects may be stored in storage system  121  along with information  131  or may be stored in a different storage system within cloud environment  102 . Data objects may be structured or unstructured data, such as a file uploaded to cloud computing environment  102  from user system  103 , a customer relationship management object, or other data, possibly organized into tables, lists, and the like, corresponding to interactions, notes, and status updates, depending on the type of service provided. 
     Accordingly, behavior information  131  corresponds to any way in which the user of user system  103  is able to change access settings, modify contents, or otherwise interact with or control one or more data objects within cloud environment  102 . Furthermore, behavior information  131  includes information about a user&#39;s interaction with the cloud environment account through which the data objects are maintained, such as user login information and account settings. Information  131  may also include historical information corresponding to the user&#39;s past behavior. Alternatively, information  131  may only include the current state and reputation analysis system may compile past states of information  131 . 
     Information  131  is used by reputation analysis system  101  to determine a reputation for the user of user terminal  103  with respect to exposure of data objects in cloud environment  102 . When used herein, exposure refers to whether a data object be retrieved, viewed, changed, or otherwise accessed—including combinations thereof—by users, systems, or applications beyond what may be desired by the user of user system  103  or other interested party, such as that user&#39;s employer. For example, a cloud storage service may provide public folders in which anyone who finds the folder can access files stored therein. Thus, the exposure for a data object that a user has stored in a public folder may be considered greater than the exposure for that same data object stored in a private folder and shared only with a limited number of people. 
     To assist in preventing a user from overly exposing data objects, the user reputation determined by reputation analysis system  101  indicates the user&#39;s reputation for overly exposing data objects. For example, a user that tends to overly expose data objects will be given a reputation by reputation analysis system  101  that indicates that tendency. Likewise, a user that does not expose data objects will be given a reputation by reputation analysis system  101  that indicates that tendency. The reputation of the user may then be used to mitigate the over exposure of data objects in cloud environment  102 . 
       FIG. 2  illustrates the operation  200  of cloud computing system  100  to determine user reputation regarding data object exposure. The method of operation includes reputation analysis system  101  receiving, from cloud computing environment  102 , information  131  regarding behavior of a user in cloud computing environment  102  (step  201 ). Information  131  is analyzed by reputation analysis system  101  to determine a plurality of exposure characteristics for the user (step  202 ). Based on the exposure characteristics, reputation analysis system  101  determines a reputation of the user for exposing data objects in cloud computing environment  102  (step  203 ). 
     The information may be received over a communication network or may be received in any other manner by which a computer system could receive data, including reading the information off of optical disks or other type of removable media. Additionally, the functionality of reputation analysis system  101  may be integrated into a service of could computing environment  102 . In those situations, the information may be received from other elements of the service. 
     The exposure characteristics may include any include items of the information, or derived from the information, that explicitly or implicitly indicate the exposure of data objects associated with the user. As data object is considered to be associated with the user if the user in some way is able to control access to the object or otherwise manipulate the object. For example, the user may have directed the creation of the data object in cloud environment  102  or was otherwise given the ability to control the object. Furthermore, the exposure characteristics considered by reputation analysis system  101  may only be exposure characteristics that are under the control of the user. In this manner, characteristics that are not the result of the user&#39;s behavior are not factored into the user&#39;s reputation. 
     Examples of exposure characteristics include a number of container objects created by the user, a number of objects created by the user in cloud environment  102 , a ratio of the number of container objects (e.g. folders) created by the user to the number of non-container objects created by the user, the average number of nested containers, an aggregate number of actions (e.g. downloads, preview, sync, edits, creates etc.) performed across all data objects, average number of unique actions performed for each data object, a number of days elapsed since any particular action was performed on any data object, an aggregate number of days since last login to the user&#39;s account, an aggregate number of logins, an aggregate number of failed logins, an aggregate number of devices that the user has logged in from, a time of the day the user has accessed cloud environment  102 , a number of countries where the user has accessed cloud environment  102 , countries from where the user has access cloud environment  102 , an average number of collaborators that the user has invited per data object, an aggregate number of collaborators that the user has invited across all data objects, an average number of external collaborators (users who don&#39;t belong to an entity with which the user&#39;s account is associated) that the user has invited to collaborate per data object, an aggregate number of collaborators that the user has invited per data object, an average number of sharing actions created per data object, an aggregate number of sharing actions across all data objects, an average number of times a data object created by the user has been accessed via actions, an average number of times a data object created by the user has been accessed by an external user, a business role of the user, a total number of data objects to which the user has access, total number of data objects not owned by the user to which the user has access, characterizations of data objects to which the user has access (e.g. legal, financial, high or low sensitivity, etc.), a number of direct reports the user has, groups that the user is a member of within an entity associated with the user&#39;s cloud environment account, a number of false positive reports in the system for this user, whether the user is an employee/contractor/customer of the entity, a number of violations in the past, or any other characteristic that could explicitly or implicitly indicate data object exposure. 
     The user reputation is calculated from the exposure characteristics and may be expressed as a value on a number scale (e.g. 1 to 10, with 10 indicating the user has a reputation of exposing objects, or vice versa), a percentage, a rank in relation to other objects, a multi-level scale (e.g. poor, satisfactory, good, best), or any other way in which a reputation may be expressed. Reputation analysis system  101  may weigh different exposure characteristics differently when determining the indication. For example, the number of untrusted users allowed to access a data object may affect the user&#39;s reputation more so than the number of user systems through which the user accesses his or her account. In some examples, these weights may be determined based on rules that indicate importance of certain characteristics over others. Additionally, the reputation may be separated into multiple distinct reputations that each indicate reputations relative to specific criteria. For example, one reputation may correspond to overall exposure in cloud environment  102  while another reputation may correspond to exposure relative to a regulatory scheme, governmental or otherwise. Similarly, the multiple reputations may relate to different types of exposure. For example, one reputation may relate to public exposure of data objects while another reputation may relate to exposure of data objects to other departments within the same entity. 
     Additionally, one or more of the reputations generated for a user may factor in or indicate policy compliance by the user. The policy may apply to the number of collaborators, privacy setting requirements for data objects, the number of instances of the same data object allowed, limits on domains from which objects can be accessed, limits on countries from where objects can be accessed, guidelines on sharing with users having high risk behavior in cloud environment  102  or are no longer with the company, the volume of files uploaded at one time, non-business hour activity, or any other action that the user may take with respect cloud environment  102 . Thus, generally, the policies may be compliance policies, acceptable use policies, HR policies, financial policies, intellectual property policies, anomalous download policies, or the like. As with other user reputation factors, the compliance with these policies is determined from the exposure characteristics. Furthermore, in addition to generating a reputation regarding policy, reputation analysis system  101  may also indicate which policies have be violated so that remedial actions can be taken. 
     Advantageously, the embodiments described above allow reputation analysis system  101  to indicate the reputations of a user for exposing objects stored in cloud computing environment  102 . This reputation may be supplied to the user or another interested party, such as an entity or enterprise associated with the user, which includes third parties. The interested party will therefore be aware of the user&#39;s reputation that it would not have known about otherwise. The interested party may then, if necessary, take steps to improve the user&#39;s reputation. 
     Referring back to  FIG. 1 , reputation analysis system  101  comprises a computer system and communication interface. Reputation analysis system  101  may also include other components such as a router, server, data storage system, and power supply. Reputation analysis system  101  may reside in a single device or may be distributed across multiple devices. Reputation analysis system  101  is shown externally to cloud computing environment  102 , but system  101  could be integrated within the components of cloud computing environment  102 . Reputation analysis system  101  could be an application server, a personal workstation, a service node, or some other network capable computing system—including combinations thereof. 
     Cloud computing environment  102  comprises a computer system, communication interface and storage system  121 . Cloud computing environment  102  may also include other components such as a router, server, and power supply. Cloud computing environment  102  may reside in a single device or may be distributed across multiple devices. Cloud computing environment  102  may include multiple cloud services each provided by multiple systems and not necessarily co-located or operated by a common entity (e.g. may include a cloud storage service provided by one company and a customer management service provided by another company). Cloud computing environment  102  could be an application server, service node, or some other computing system—including combinations thereof. 
     User system  103  comprises a computer system and a communication interface. User system  103  may also include a user interface, memory device, software, processing circuitry, or some other communication components. User system  103  may be a telephone, tablet, computer, e-book, mobile Internet appliance, wireless network interface card, media player, game console, or some other end user computing apparatus—including combinations thereof. 
     Communication links  110 - 111  could use various communication protocols, such as Time Division Multiplex (TDM), Internet Protocol (IP), Ethernet, communication signaling, Code Division Multiple Access (CDMA), Evolution Data Only (EVDO), Worldwide Interoperability for Microwave Access (WIMAX), Global System for Mobile Communication (GSM), Long Term Evolution (LTE), Wireless Fidelity (WIFI), High Speed Packet Access (HSPA), or some other communication format—including combinations thereof. Communication links  110 - 111  could be direct links or may include intermediate networks, systems, or devices. 
       FIG. 3  illustrates cloud computing system  300 . Cloud computing system  300  includes reputation analysis system  301 , cloud computing service  321 , cloud computing service  322 , user systems  341 - 343 , and communication network  303 . Cloud computing service  321  includes object storage  331  that stores user behavior information  371  data objects  380 ,  381 ,  382 , and  383 . Similarly, cloud computing service  322  includes object storage  332  that stores user behavior information  372  and data objects  384 ,  385 ,  381 , and  386 . 
     Communication network  303  comprises network elements that provide communications services to reputation analysis system  301 , cloud services  321 - 322 , and user systems  341 - 343 . Communication network  303  may comprise switches, wireless access nodes, Internet routers, network gateways, application servers, computer systems, communication links, or some other type of communication equipment—including combinations thereof. Communication network  303  may include wide area networks, local area networks, the Internet, or other types of networks—including combinations thereof. 
     In operation, users associated with entity  304  operate user systems  341 - 343 . User systems  341 - 343  may be provided to the users by entity  304  or may otherwise be able to access services to which entity  304  subscribes. In this example, entity  304  is a business enterprise and the users are employees of that business. Entity  304  subscribes to services provided by cloud services  321  and  322  for the employees of entity  304 . Cloud services  321  and  322  may be otherwise unrelated cloud services operated by independent entities but, for the purposes of this example, are considered part of cloud computing environment  302  for entity  304 . Each employee receives individual login information to individual accounts on each of services  321  and  322  and can use each of the services to the extent that entity  304  is subscribed (e.g. within the limits of entity  304 &#39;s service agreement). 
     To help ensure no sensitive information becomes overly exposed during the employees&#39; use of cloud services  321  and  322 , entity  304  uses reputation analysis system  301  to inform entity  304  about each employee&#39;s reputation for exposing data objects. Reputation analysis system  301  may be controlled directly by entity  304  (i.e. may be owned or operated by entity  304 ) or entity  304  may subscribe to services provided by reputation analysis system  301  through a third party operator of reputation analysis system  301 . 
       FIG. 4  illustrates operation  400  of cloud computing system  300  to determine user reputation regarding data object exposure. Specifically, operation  400  illustrates the generation of a user reputation for the employee operating user system  343 . A similar method is used to generate reputations of other employees. Additionally, it should be understood that a single employee might use more than one user system to access data objects in cloud service. For example, an employee may access cloud services  321  and  322  using a phone, tablet, personal computer, and a web interface. 
     Operation  400  begins with the employee using user system  343  creating objects  380 - 386  in the employee&#39;s account in cloud services  321 - 322  as pictured in  FIG. 3 . To create the objects, the objects may be uploaded to cloud services  321 - 322 , may be generated at cloud services  321 - 322  in response to directions from user system  343 , or may become stored in cloud service  321 - 322  by some other means. One particular data object  381  is located in both cloud service  321  and cloud service  322 . 
     Reputation analysis system  301  then uses Application Programming Interface (API) calls for each of cloud services  321 - 322  to request user behavior information  371  regarding the employee&#39;s behavior in each of cloud services. For example, the user behavior information  371  and  372  may include accessibility settings, metadata, or any other type of information about data objects  380 - 386 . User behavior information  371  and  372  may further include information that does not directly relate to a specific data object, such as activity logs, which include account login history and the like. While behavior information  371  is shown as stored in object storage  331 , behavior information  371  may be stored in a separate storage system within cloud service  321 . Similarly, while behavior information  372  is shown as stored in object storage  332 , behavior information  372  may be stored in a separate storage system within cloud service  322 . 
     The API used for each of cloud services  321 - 322  may include the same API calls typically used by user systems to interact with the cloud services. For example, there may be an API call for one or more of cloud services  321 - 322  that allows a user to request with whom a particular data object is shared. In some examples, even though entity  304  is subscribing to cloud services  321 - 322  on behalf of its employees, one or more of cloud services  321 - 322  may not allow entity  304  to use certain API calls to access user specific information. In those cases, reputation analysis system  301  impersonates individual users so that reputation analysis system  301  can request the information. The impersonation may be performed with the consent of the user since the user is an employee of entity  304  and is using a service subscribed to by entity  304 . 
     In some embodiments, one or more of cloud services  321 - 322  may provide APIs that allow code to execute within the cloud service on behalf of reputation analysis system  301 . In those examples, the code may transfer information  371  and  372  to reputation analysis system  301  periodically or upon certain triggers. For example, upon settings for a particular object being changed, the code may instruct the cloud service to transfer a notification of those changes to reputation analysis system  301 . 
     Once the requested behavior information  371  and  372  is received by reputation analysis system  301 , reputation analysis system  301  analyses the information in accordance with operation  500  in  FIG. 5 . In particular, reputation analysis system  301  identifies exposure characteristics from the received behavior information  371  and  372  (step  501 ). Rules are then applied to the identified exposure characteristics (step  502 ). The rules may be business rules identified by entity  304  based on exposure characteristics that entity  304  believes are desirable or relate to a policy. 
     Based on how the exposure characteristics satisfy the rules, reputation analysis system  301  determines a reputation for the employee operating user system  343  (step  503 ). For example, if the employee&#39;s behavior as indicated by the exposure characteristics fall within the parameters of the rules, then the employee&#39;s reputation for exposing data objects is generated to indicate a good reputation. Alternatively, if the rule application to the exposure characteristics indicates that the employee has allowed more than a defined number of sensitive data objects to be accessed by untrusted individuals, then the employee&#39;s reputation is generated to indicate a poor reputation. 
     In some embodiments, reputation analysis system  301  continues to receive updates to information  371  and  371 , which allows reputation analysis system  301  to adjust the reputation of the employee based on the newly received information. Thus, the employee&#39;s reputation may be bettered or worsened depending upon how exposure characteristics identified from the new information comply with the rules. 
     Upon determining the reputation of the employee, reputation analysis system  301  may notify entity  304  of the reputation. For example, a notification may be sent to an administrator within entity  304  via email, a dedicated application for interacting with reputation analysis system  301 , a web interface to reputation analysis system  301 , or by some other notification means. A notification may further be sent to the employee for which the reputation is generated (i.e. the employee operating user system  343  in this embodiment). 
     In some embodiments, the notification may be presented as part of a software extension over the application sued to access one or more of cloud services  321  and  322 . For example, if cloud services are accessed through a browser interface, a browser extension may overlay information on the cloud service interface or modify the display of the cloud service interface to indicate the reputations. 
     In some examples, the notification only indicates the reputation of the employee if the reputation meets a threshold reputation criteria (e.g. if the reputation is worse than a desired reputation threshold). If the employee&#39;s reputation does not meet the threshold, then the notification is either not generated for the single employee or that single employee&#39;s reputation is not included in a notification having reputations of multiple employees. The notification may also indicate specific reasons as to why an employee received his or her reputation was determined (e.g. which exposure characteristics did not satisfy the rules). Such an indication may allow an employee, administrator, or other interested party, to change the offending characteristics to improve the reputation. Similarly, in some examples, the notification may explicitly set forth recommended actions that can be taken to improve the employee&#39;s reputation. These actions may include both automatic (which may also be performed by system  301  without notification) and manual remedial actions that should improve the employee&#39;s reputation. For example, the actions may include changing access settings for exposed objects, such as disabling a public link to the objects, expiring the link, remove access from certain users, modifying or encrypting the data objects, deleting the data objects, changing login passwords, changing user privileges (e.g. not allowing the user to share data objects), or notifying the data owner or manager. Reputation analysis system  301  may also log the notification data in an incident module for future access. 
     Using reputation analysis system  301  in the manner described above, entity  304  can better ensure that its employees are not using cloud computing services  321 - 322  in a manner that would expose information that entity  304  would not want disclosed. 
       FIG. 6  illustrates a more detailed version of cloud computing service  321 . Specifically,  FIG. 6  illustrates a blow up of data object  381  on cloud service  321 . Data object  381  includes object data  600 , settings  601 , and metadata  602 . While not necessarily stored separately from object data  600 , settings  601  and metadata  602  are included in behavior information  371  as indicated by the arrow. Data objects  380 ,  382 , and  383  include settings and metadata as well, which may also be included in information  371 , but are not shown for clarity. While settings  601  and metadata  602  are shown as being part of data object  381 , cloud service  321  may maintain settings  601  and metadata  602  elsewhere. For example, cloud service  321  may maintain a separate data structure for storing such information in association with each data object. As noted above, that separate data structure may also include other data comprising information  371 . When reputation analysis system  301  requests information  371  in accordance with operation  400 , cloud service  321  transfers at least a portion of settings  601  and metadata  602  as part of information  371  in response to the request. 
     In some embodiments, depending on whether entity  304  allows reputation analysis system  301  to access object data, at least a portion of object data  600  may also transferred as part of information  371  to reputation analysis system  301  for use when determining the employee&#39;s reputation. For example, reputation analysis system  301  may consider object data  600  when determining whether the exposure of data object  381  adversely affects the employee&#39;s reputation. 
       FIG. 7  illustrates a more detailed version of cloud computing service  322 . Specifically,  FIG. 7  illustrates a blow up of data object  381  on cloud service  322  rather than cloud service  321 . Data object  381  includes object data  600 , settings  701 , and metadata  702 . While not necessarily stored separately from object data  600 , settings  701  and metadata  702  are included in behavior information  372  as indicated by the arrow. Data objects  384 ,  385 , and  386  include settings and metadata as well, which may also be included in information  372 , but are not shown for clarity. Since cloud service  322  is separate from cloud service  321 , while object data  600  may be the same on cloud service  322 , settings  701  and metadata  702  are different for cloud service  322  than settings  601  and metadata  602  for cloud service  321 . The settings and metadata may be different due to the differences in cloud computing platforms between services  321  and  322 . Likewise, the settings and metadata may be configured to have different exposure levels on each service. 
     As is the case with cloud service  321 , while settings  701  and metadata  702  are shown as being part of data object  381 , cloud service  322  may maintain settings  701  and metadata  702  elsewhere. As noted above, that separate data structure may also include other data comprising information  371 . When reputation analysis system  301  requests information  372  in accordance with operation  400 , cloud service  322  transfers at least a portion of settings  701  and metadata  702  as part of information  372  in response to the request. 
     In some embodiments, if reputation analysis system  301  is allowed to receive object data  600 , reputation analysis system  301  uses object data  600  to classify data object  381  as being related to a particular business unit, topic, sensitivity, or other form of classification. For example, reputation analysis system  301  may review the contents of object data  600  to determine that data object  381  is a legal document that should not be shared outside of entity  304 &#39;s legal department for any reason in accordance with the rules. From this classification, if the data object  381  is available for access by individuals outside of the legal department, reputation analysis system  301  may generate a poorer reputation for exposure than if data object  381  was not so accessible. 
     Furthermore, the classification of data objects into particular groupings (e.g. business unit) allows for employee reputations to be separated into reputations pertaining to each grouping. A notification message indicating a group specific reputation can be directed to an administrator in charge of the data maintained in that grouping rather than an administrator in charge of all data. Similarly, such an administrator may receive notifications that represent reputations in the aggregate for multiple employees that manage data objects of that grouping in cloud services  321  and  322 . 
     In other examples, reputation analysis system  301  is able to classify data object  381  without having access to object data  600 . In those examples, a user may aid reputation analysis system  301  by indicating a classification or reputation analysis system  301  may infer a classification based on other information, such as the name of a container (e.g. folder) for data object  381 , a position of the employee (e.g. a legal department employee likely manages legal documents), or other information that may suggest a classification for a data object. 
     In some embodiments, if a separate reputation is determined for the employee in each of cloud services  321  and  322 , then a notification may be generated if the indications of exposure differ by more than a threshold. The notification may simply indicate the anomaly or may indicate specific exposure characteristics that caused the difference in reputations. For example, if the reputation is represented as a number on a scale of 1 to 10, with 1 indicated that the employee has the worst possible reputation for exposing objects, reputation analysis system  301  will generate a notification if the employee has a rank of 3 in one service and 8 in the other. This notification may be presented to any individual or system to which notifications may be sent as discussed elsewhere herein. 
       FIG. 8  illustrates at least a portion of the information  371  and  372  that may be transferred to reputation analysis system  301  in operation  400 . Upon receiving information  371  and  372 , reputation analysis system  301  identifies exposure characteristics and analyzes the characteristics using rules in operation  500  described above. 
     Information  371  includes account information  801  for the employee in cloud service  321 . Account information  801  indicates that the employee&#39;s account has 5 daily logins. The number of daily logins may be an average number of logins over a multi-day period, may be a number of logins for the current day up to the time information  371  is requested, may be a number of logins for the previous full day, or some other possible measure of daily logins. Additionally, account information  801  indicates that the number of user systems that access the account is 3 and the number of data objects maintained for the account is 26. 
     Information  371  further includes settings  601  and metadata  602 . Settings  601  indicate that data object  381  is under user John Doe&#39;s account (from which reputation analysis system  301  may be able to determine other employee information), is being shared with 2 other users (some examples may identify those users and indicate whether those other users are employees of entity  304 ), and is not located in a publicly accessible folder. Metadata  602  indicates that object data  381  is a file, it was last accessed 3 days ago, and is a Human Resources (HR) file. It should be understood that information  371  may also include settings and metadata for other objects, which are not shown for clarity. 
     Information  372  includes account information  802  for the employee in cloud service  322 . Account information  322  indicates that the account has 32 daily logins, that 8 different user systems have accessed the account, and that the account maintains 87 data objects. Additionally, information  372  includes settings  701  and metadata  702 . Settings  701  indicate that data object  381  is being shared with no other users but is located in a publicly accessible folder. Metadata  702  indicates that object data  381  is a file, it was last accessed 22 minutes ago, and is located in a folder labeled miscellaneous. 
     From on information  371  and  372 , reputation analysis system  301  identifies exposure characteristics. An exposure characteristic may simply comprise an item of information  371  and  371 , such as the number of user systems that have accessed the account. Alternatively, some of the exposure characteristics will be identified by reputation analysis system  301  by deriving the exposure characteristic from the information. For example, an exposure characteristic may be an aggregation of settings for multiple data objects. Thus, reputation analysis system  301  may need to perform calculations on items (e.g. adding, averaging, etc.) of the received information to identify the exposure characteristic. Similarly, reputation analysis system  301  may need to process previously received items of information  371  and  372  to arrive at various exposure characteristics. For example, if the number of daily logins merely reflects the number of daily logins on a most recent day, then reputation analysis system  301  would need to track previously received numbers of daily logins in order to generate an average number of daily logins over a period of time, as may be one exposure characteristic. 
     Once the exposure characteristics have been identified, reputation analysis system  301  determines one or more reputations of the employee for exposing data objects in each of cloud environments  321  and  322 . Alternatively, or in addition to the two individual reputations, an aggregate reputation for the employee across both services may also be determined. 
     Depending on thresholds and characteristics defined in rules applied by reputation analysis system  301 , the exposure characteristics identified from information  371  and  372  may adversely impact the reputations determined by reputation analysis system  301 . In particular, the rules may indicate that both the number of daily logins and the number of user systems in account information  802  are above a desired threshold. Moreover, the rules may indicate that object  381  is over exposed for being in a public folder according to settings  701 . Therefore, reputation analysis system  301  generates a reputation for the employee&#39;s exposure behavior in cloud service  322  as being relatively poor (e.g. 3 on a scale of 1-10 with 10 being the best reputation an employee may receive for not exposing objects). In contrast, the exposure characteristics in information  371  satisfy the rules to the point that reputation analysis system  301  generates a good reputation for behavior in cloud service  321  (e.g. 8 on the scale of 1-10). Similarly, a third aggregate reputation may be generated for the employee to represent the employee&#39;s reputation across both cloud services. This aggregate reputation may be somewhere in between the two example reputations above (e.g. 6 on the scale of 1-10). 
     After generating the reputations, reputation analysis system  301  then notifies entity  304  of the reputations for the employee. Entity  304  is then aware of the employee&#39;s reputation and can take actions to improve the employee&#39;s reputation to prevent unnecessary data object exposure. 
       FIG. 9  illustrates reputation analysis system  900 . Reputation analysis system  900  is an example of reputation analysis systems  101  and  301 , although systems  101  and  301  may use alternative configurations. Reputation analysis system  900  comprises communication interface  901 , user interface  902 , and processing system  903 . Processing system  903  is linked to communication interface  901  and user interface  902 . Processing system  903  includes processing circuitry  905  and memory device  906  that stores operating software  907 . 
     Communication interface  901  comprises components that communicate over communication links, such as network cards, ports, RF transceivers, processing circuitry and software, or some other communication devices. Communication interface  901  may be configured to communicate over metallic, wireless, or optical links. Communication interface  901  may be configured to use TDM, IP, Ethernet, optical networking, wireless protocols, communication signaling, or some other communication format—including combinations thereof. 
     User interface  902  comprises components that interact with a user. User interface  902  may include a keyboard, display screen, mouse, touch pad, or some other user input/output apparatus. User interface  902  may be omitted in some examples. 
     Processing circuitry  905  comprises microprocessor and other circuitry that retrieves and executes operating software  907  from memory device  906 . Memory device  906  comprises a non-transitory storage medium, such as a disk drive, flash drive, data storage circuitry, or some other memory apparatus. Operating software  907  comprises computer programs, firmware, or some other form of machine-readable processing instructions. Operating software  907  includes exposure characteristics analysis module  908  and user reputation module  909 . Operating software  907  may further include an operating system, utilities, drivers, network interfaces, applications, or some other type of software. When executed by circuitry  905 , operating software  907  directs processing system  903  to operate reputation analysis system  900  as described herein. 
     In particular, exposure characteristics analysis module  908  directs processing system  903  to receive, from a cloud computing environment, information regarding behavior of a user in the cloud computing environment. Module  908  further directs processing system  903  to analyze the information to determine a plurality of exposure characteristics for the user. User reputation module  909  directs processing system  903  to determine a reputation of the user for exposing data objects in the cloud computing environment based on the plurality of exposure characteristics. 
     The above description and associated figures teach the best mode of the invention. The following claims specify the scope of the invention. Note that some aspects of the best mode may not fall within the scope of the invention as specified by the claims. Those skilled in the art will appreciate that the features described above can be combined in various ways to form multiple variations of the invention. As a result, the invention is not limited to the specific embodiments described above, but only by the following claims and their equivalents.