Automated role and entitlements mining using network observations

A role and entitlements mining system uses network intelligence to facilitate role definition. The system records traffic on a network. The traffic is analyzed to identify the user and application involved. The matched data is then provided to an analytics engine, which analyzes that data to attempt to derive an initial set of one or more roles and the application entitlements for each role. Each role derived by the analytics engine identifies one or more users who are identified as belonging to the role, as well as one or more application entitlements. Preferably, one or more directory services are then interrogated for known group and user relationships to detect whether the roles identified by the analytics engine can be modified or enriched. Evaluation of the known group and user relationships provides a way to identify a more granular set of role definitions. A role-based access control policy is then generated.

BACKGROUND

1. Technical Field

This disclosure relates generally to security systems for computer networks.

2. Background of the Related Art

When defining an access control policy for an organization a common approach is to implement Role Based Access Control (RBAC). Using RBAC, entitlements to perform particular functions are assigned to roles instead of directly to users. The way a user gets the entitlements is by being assigned to roles or requesting membership of the role. Thus, to define an RBAC-based policy for an organization, an understanding of the user roles and the entitlements for those roles is required. Once this information is understood for an organization, the appropriate RBAC policy can be defined and deployed to an Identity Management system, such as IBM® Tivoli® Identity Manager™. Use of such tools greatly simplifies access management as compared with directly defining a user-to-entitlement relationship.

A problem, however, is how to create an initial RBAC policy for an organization. A particular issue is how to discover the roles and the entitlements for those roles, especially in a large organization. There are a number of common approaches that together are used to address these issues and to facilitate the defining of the RBAC policy. A typical approach involves several manual steps including interviewing each employee (about their job function and application entitlements), interviewing management (asking their view on employee roles and entitlements), performing system review (e.g., examining application user directories for user entitlements such as group membership), reviewing documentation (e.g., examining documents that define the management structure of an organization and job definitions), and evaluating best practices (i.e., trying to use roles and entitlements defined in other organizations and then tailoring these to the current enterprise). These approaches, however, are very expensive and time-consuming, and they rarely result in a meaningful RBAC policy. Indeed, it is not uncommon for an enterprise to embark on a role-defining exercise and then much later decide that no useful policy has resulted (in which case the entire exercise may then be abandoned). This is because manual processes just are not efficient at discovering the true roles and entitlements of an organization.

Another problem is that, with respect to an identity solution, the definition of what is or is not a role is often confusing (especially to individuals being questioned about employee “roles”), because most organizations use the term to identify job description rather than the person's need for access to systems. Indeed, some individuals being interviewed about roles may not even understand or appreciate the distinction (between an IT-based RBAC role and an employment role), which further skews any results toward a negative or un-useful outcome.

It is also known in the art to provide automated organizational role modeling for role based access controls. This approach inspects permission settings within IT systems, and these settings are then analyzed to determine whether there are any patterns within these permissions. Automated role modeling using the above-described approach, however, tends to produce roles that track existing policy set by the organization as opposed to roles that more closely indicate the actual behavior of users within the organization. Another approach, which typically is implemented in identity management systems, provides for role mining capabilities and the ability to model new roles based on policy information. That approach, however, typically relies upon static information.

There remains a need in the art to provide enhanced role and entitlements mining that addresses these and other problems.

BRIEF SUMMARY

According to this disclosure, role mining is facilitated using network observations. In particular, network traffic inspection technology records user access to one or more applications. This information is then used to determine the roles and associated entitlements in use within the Information Technology (IT) services within an organization. According to this approach, a traffic collection system records traffic on a network. The traffic on a particular connection is then analyzed to identify the user and application involved. The matched data is then provided to an analytics engine, which analyzes that data to attempt to derive one or more roles (and the application entitlements for each role). Each role derived by the analytics engine identifies one or more users who are identified as belonging to the role, as well as one or more application entitlements. Preferably, existing directory services (whether local or remote) are then interrogated for known group and user relationships to detect whether the roles identified by the analytics engine (as determined from the collected network traffic) should be modified or enriched. Evaluation of the known group and user relationships provides a way to identify a more granular set of role definitions. At this step, the existing directory service is accessed and the actual groups for the users (that have been associated with the role) are extracted and evaluated; based on this evaluation, the role entitlements may be changed or augmented. From this process, which is preferably automated, an output in the form of an XML role-based access control policy is generated. This RBAC policy may then be provided to an identity management system to create an initial role and entitlements policy for the system.

The foregoing has outlined some of the more pertinent features of the invention. These features should be construed to be merely illustrative. Many other beneficial results can be attained by applying the disclosed invention in a different manner or by modifying the invention as will be described.

DETAILED DESCRIPTION OF AN ILLUSTRATIVE EMBODIMENT

As will be seen, the techniques described herein may operate in conjunction within the standard client-server paradigm such as illustrated inFIG. 1in which client machines communicate with an Internet-accessible Web-based portal executing on a set of one or more machines. End users operate Internet-connectable devices (e.g., desktop computers, notebook computers, Internet-enabled mobile devices, or the like) that are capable of accessing and interacting with the portal. Typically, each client or server machine is a data processing system such as illustrated inFIG. 2comprising hardware and software, and these entities communicate with one another over a network, such as the Internet, an intranet, an extranet, a private network, or any other communications medium or link. A data processing system typically includes one or more processors, an operating system, one or more applications, and one or more utilities. The applications on the data processing system provide native support for Web services including, without limitation, support for HTTP, SOAP, XML, WSDL, UDDI, and WSFL, among others. Information regarding SOAP, WSDL, UDDI and WSFL is available from the World Wide Web Consortium (W3C), which is responsible for developing and maintaining these standards; further information regarding HTTP and XML is available from Internet Engineering Task Force (IETF). Familiarity with these standards is presumed.

By way of further background, role-based access control (RBAC) for enforcing security within an enterprise is well-known in the prior art. Typically, a role is a set of permissions that are enabled for an organizational agent that performs certain job functions. Role engineering is the process of defining a set of roles for the organization and assigned permissions to those roles. Role engineering may be carried out in a top-down manner, or a bottom-up manner. In a top-down approach, roles are defined by analyzing business processes and identifying the one or more functions that comprise each such process; a set of permissions on information systems are then associated with each function. Typically, the top-down approach begins by defining a job function; a role is then created for this function by associating whatever permissions are needed. Role mining can be used in conjunction with this top-down approach to identify proposed roles that can be examined to determine if they satisfy the business process function to which they might be associated. In contrast, in a bottom-up approach, existing permission assignments are used to formulate roles. Typically, one or more permission assignments are aggregated and associated with a role, and the process may be automated for efficiency.

A typical RBAC model is based on role names, and assignment of users to those roles. The associated access control is based on these roles. An RBAC-based role typically has meaning only within a given context (i.e., within a Company in which the role is defined, with respect to a given application system used in the Company, or the like). Moreover, role-based access control usually requires centralized management of user-to-role and permission-to-role assignments and, as a consequence, it is not well-suited for distributed environments.

RBAC policy is used to provision accounts to end systems. As part of this provisioning exercise, and as described generally above, users may be placed into groups. As noted, the end systems typically rely on group membership to determine what access rights that a particular user will have within the system/application.

Automated Role and Entitlements Mining Using Network Observations

With the above as background, the techniques of this disclosure are now described. As described above, according to this approach role mining is facilitated using network observations. In particular, preferably network traffic inspection technology is used or leveraged to record user access to one or more applications. This information is then used to determine the roles and associated entitlements in use within an information technology (IT) system, preferably by augmenting or enriching roles (defined using the network traffic data) with information about group and user relationships. The result is a role-based access control policy that may then be enforced in the enterprise by known systems, such as an identity manager.

As will be described, a role and entitlements mining system according to this disclosure records traffic on a network. The traffic is analyzed to identify the user and application involved. The matched data is then provided to an analytics engine, which analyzes that data to attempt to derive an initial set of one or more roles and the application entitlements for each role. Each role derived by the analytics engine identifies one or more users who are identified as belonging to the role, as well as one or more application entitlements. Preferably, one or more directory services are then interrogated for known group and user relationships to detect whether the roles identified by the analytics engine can be modified or enriched. Evaluation of the known group and user relationships provides a way to identify a more granular set of role definitions. A role-based access control policy is then generated.

FIG. 3illustrates the basic components of an automated role and entitlements mining system300that implements the above-described functionality. The system300comprises a set of sub-systems that may be implemented together in an integrated manner, or by executing the individual sub-systems separately. Each sub-system may comprise one or more components or functions that are implemented in one or more machines, devices, programs, processes or other computing entity. Sub-systems (or individual entities therein) may be co-located or remote from one another, and the functionality of a particular sub-system (or an individual entity therein) may be shared. Thus, and generalizing, the various features and functions of the system300may be implemented by any combination of hardware, software, machines, devices, operating environments (including cloud-based), and the like, as necessary or desired.

Each of the sub-systems (or, more generally, the functions) of the automated role and mining system of this disclosure are now described.

As noted above, the role mining technique of this disclosure uses network intelligence to inform the role definition process. To this end, the traffic collection and storage sub-system302is used to record traffic on a network305, such as an enterprise network. The particular type of network is not limited, however, as the approach herein may be implemented on or in association with any network within or associated with a set of users and applications for which the role mining is desired to be accomplished. Thus, the network may be an internal network, a network partially within an organization boundary, a private network, a wireless or wire-line network, or the like. The traffic collection and storage sub-system302preferably uses technology (whether custom or off-the-shelf) that can identify and extract related connections, sessions and packets. One type of off-the-shelf technology that may be used for this purpose is known as deep packet inspection (DPI) or deep session inspection (DSI). DPI/DSI inspection engines are known in the art and are an example of a device that performs traffic capture and initial summary analysis of the captured data. Preferably, the data collected by the traffic analysis is persisted in a data store or storage sub-system, such as a relational database, data array, linked list, or the like. Typically, the following information is stored for each connection: source IP address and port number, destination IP address and port number, and HTTP/S request information (for web requests) or partial traffic capture for other types of applications (e.g., the first 100 data packets for a thick client application). Of course, the nature and type of information that are collected by the traffic collection and storage sub-system302will vary depending on the type of network, client, and application. This sub-system thus provides traffic intelligence that will be used to generate an initial set of roles (sometimes referred to herein as “candidate” roles). As noted above, the traffic collection and storage sub-system is not required to be on a physical network, and it may also be implemented as a software component running inside a virtualized network environment on a large server.

In particular, and referring back toFIG. 3, the user and application matching sub-system304takes the summary information for each connection (as identified and provided by the traffic collection sub-system302), and it analyzes that information to identify the user and application involved. Identification of the user and application data then facilitates the creation of the initial set of roles and entitlements, as will be described. To identify the user creating the network traffic, preferably the user and application matching sub-system304implements one or more techniques to map the source IP address data to a real user identity. One approach identifies the user from authentication data, which data may be obtained from some network authentication device or system (e.g., login events at a directory service (e.g., Active Directory) that shows the user identity and an associated IP address (e.g., a Network Authentication Event). In the alternative, the authentication data may be obtained by manual intervention, e.g., by having the user authenticate to the traffic collection sub-system itself. Yet another alternative is to use or leverage a systems management process to provide relevant data, such as a binding of a source IP address to a MAC address on a computer associated with a user having a one or more accounts in the enterprise. Regardless of which technique is used (and these techniques are not intended to be limiting), the result is a user identifier, such as a name, userID, or the like.

The user and application matching sub-system304identifies the one or more application(s) in use (as evidenced by the traffic data) using one or more techniques. Thus, in one approach, the application is identified by offline inspection, e.g., using a web matching engine that identifies that some request URL matches a particular application or application part. Another approach is to implement a real-time inspection to analyze characteristics of the traffic and thus ascertain the application. Regardless of which approach is used (and these approaches are not intended to be limiting), the result is an application identifier.

The user and application determination may be performed by separate entities, or a single composite entity that comprises the user and application matching sub-system304. A next-generation (next-gen) firewall is an example of a device that performs both user and application matching in the manner described. The output of this sub-system typically is a tuple comprising: a user identifier (e.g., Paul), a destination IP address (e.g., 10.1.2.1), and an application identifier (e.g., TAMeb®). The destination address is needed to distinguish between multiple instances of applications, as it is not unusual for an organization to have separate instances deployed and executing within the enterprise computing environment. Where multiple instances are not implemented, this data may be omitted.

Referring back toFIG. 3, the analytics grouping sub-system306comprises an analytics engine that receives that data (namely, the user and application for a particular connection) generated by the user and application matching sub-system304. Generally, this component finds patterns from client systems and matches those patterns with application use. From these patterns, roles and associated application entitlements are then derived. This type of matching or grouping is a common function to known behavioral analytics systems, which systems may be used for this purpose. The output of the analytics grouping sub-system306is a role list that identifies one or more candidate roles, wherein each role in the role list is then defined by an identifier (a name of the candidate role), a set of one or more users who have been found (by the analytics engine) to satisfy the defined role, and a set of one or more application entitlements that have been found (by the analytics engine) to satisfy the role definition.FIG. 4illustrates a representative role-list in an XML format. This particular role-list400, which is merely representative, identifies a single candidate role, called Role1. Based on the network traffic data and the pattern matching, the analytics engine has determined that the role has three (3) users (e.g., Paul, Chris and John), and several application entitlements (namely, TAMeb®, Matlab®, and e-mail). Of course, the above role is merely exemplary. As noted above, the analytics engine creates the role based on the network traffic data as processed by the user and application match data.

Referring back toFIG. 3, the role and entitlement extraction sub-system308receives the role-list and provides further processing based on user-specific application entitlements for the application. Preferably, sub-system308provides a way for the candidate roles (as observed from the network traffic) to be further refined using actual application instances in use. This operation typically requires the component to interrogate user or group repositories/directories with respect to the application(s) being monitored. To this end, preferably the sub-system is configured (e.g., upon initialization) or can otherwise obtain at run-time configuration information that identifies each directory in the organization. Each such directory typically stores information about a particular application and the user(s) or group(s) of users that have entitlements to use that application (or an application instance). A representative directory is an LDAP directory. The directory is adapted to be queried and returns user/group data concerning the application in question. For each candidate role in the role-list, the role and entitlement extraction sub-system308queries one or more directories to obtain group information such as which user(s) are members of which group(s). Based on the information returned from the directory queries, the role and entitlement extraction sub-system may determine that a particular grouping should be enforced within the role. In other words, the information returned from the directory queries may be used to augment, modify, supplement or otherwise enrich the entitlement policy set forth in the role definition.

The directory services queried are not limited to those within the organizational boundary. The role and entitlement extraction sub-system308may also query services residing beyond the organization's boundary (e.g., cloud-based, B2B, and the like).

As an example of this operation, it is assumed that the system has just the role identified in the XML snippet shown inFIG. 4. Taking the example of the TAMeb application, one or more directory servers are then probed to extract the actual groups for users, in this example users Paul, Chris and John. These probes also return one or more role identifiers that are used within the existing directory system(s). Based on the one or more queries, the sub-system has received information about these role identifiers, as well as information that, with respect to this application, Paul and Chris share the group of A and B (for the role of Principal Engineer), and Chris and John share the group of A and C (for the role of Senior Engineer). Based on the identifiers and group data returned, the sub-system then determines that these groupings should be applied to the XML entitlements policy; as a result, the sub-system308creates an enriched role list that includes several additional roles and entitlements based at least in part on the returned information. In this particular example, the resulting role-list is shown inFIGS. 5A-5B. As can be seen, this role list includes several roles: Principal Engineer, Finance, Engineer and Senior Engineer. The application entitlements for each role are identified, and user group data is applied. In particular, the Principal Engineer role includes the TAMeb application entitlements associated with user groups A and B, and the Senior Engineer role includes those entitlements associated with user groups A and C.

Preferably, all or substantially all of the application directories in the organization are interrogated to ensure that the combined role policy (such as shown inFIGS. 5A-5B) can be determined. This XML snippet output can be represented by the RBAC policy shown inFIG. 6. In this example, when loading users into a dependent system (such as the identity manager310, users Paul and Chris belong to the Principal Engineer role, and Chris and John belong to the Senior Engineer role. If necessary, an IT administrator may review the RBAC policy and make any final refinements as may be desired. Thus, for example, the administrator may rename an application name to ensure that the name matches a name used in systems that already consume this information. Additionally, the IT administrator may want to rename the role itself to something more familiar to an administrator of the identity manager.

As noted above, the RBAC policy is fed into an identity management system to create an initial role and entitlements policy for the organization or some defined portion thereof. A representative identity management system that may be used is IBM Tivoli Identity Manager.

An end-to-end system implementation of this type may be conceptualized as illustrated inFIG. 7. This system is one that consumes a policy that has been created as a result of the network observations-generated RBAC.FIG. 7in particular illustrates a representative enterprise policy management system700. The system700may be implemented across one or more machines operating in a computing environment, such as shown inFIG. 1. Typically, the system comprises a policy administration point (PAP)702, the policy decision point (PDP)704, and a policy enforcement point (PEP)706. Generally, the policy administration point702is used to define a policy, which may be specified as a set of XACML policy expressions. In the example scenario for this disclosure, the policy administration point comprises components302,304,306and308described above. As noted above, this policy uses subject attributes provided from a user repository708, as well runtime and environment data received from policy information point (PIP)710. The policy decision point (PDP)704(in this scenario the identity manager) receives similar information and responds to an XACML policy query received from the policy enforcement point (PEP)706to enforce the policy on a subject and with respect to a particular action initiated by the subject. The PDP704implements the policy decision.

Thus, according to this disclosure, network inspection techniques preferably are used to assess an initial set of roles and entitlements, which roles are then refined into a more fine-grained set of definitions using group and user relationship data obtained by interrogating one or more existing directories in the enterprise. This latter step helps to detect policy anomalies within the directory services, or within the collected traffic data. This approach (using directory information to help refine the role data determined by network traffic analysis) is advantageous, as the group information (e.g., the members of a particular group) within system directories may not always be correct and current, and it may therefore be unreliable for use in creating any initial set of roles. But such data is quite useful to help refine (as necessary) the initial set of roles and entitlements that are determined by the analytics engine.

The approach of this disclosure thus uses network intelligence to create a roles-based view of the enterprise. As has been described, the approach focuses on observing a user's behavior on one or more enterprise systems to determine a set of possible roles and their entitlements. The approach provides an automated way of discovering a set of roles and entitlements, as well as a low-cost method of creating an initial set of roles and entitlements. The approach is unobtrusive and can leverage existing computing machines, devices and functionalities. By leveraging network intelligence in the manner described, roles and entitlements are created based on real behavior rather than perceived behavior or documented policies. The approach also enforces a separation between the concepts of IT roles for application access and business roles. Once the RBAC policy has been generated as described, it may be readily applied in an identity management system to create an initial role and entitlements policy.

The role and entitlements mining functionality described above may be implemented as a standalone approach, e.g., a software-based function executed by a processor, or it may be available as a managed service (including as a web service via a SOAP/XML interface). The particular hardware and software implementation details described herein are merely for illustrative purposes are not meant to limit the scope of the described subject matter.

The technique described herein also may be implemented in or in conjunction with various server-side architectures including simple n-tier architectures, web portals, federated systems, and the like. The techniques herein may be practiced in a loosely-coupled server (including a “cloud”-based) environment.

In a representative embodiment, the process flows described above are implemented in a special purpose computer, preferably in software executed by one or more processors. The software is maintained in one or more data stores or memories associated with the one or more processors, and the software may be implemented as one or more computer programs. Collectively, this special-purpose hardware and software comprises or supplements an existing intrusion detection system solution.

Without meant to be limiting, preferably a management server management console exposes one or more web-based interfaces that may be used to configure, manage and administer the role and entitlements mining system.

The described functionality may be implemented as an adjunct or extension to an existing identity management solution, product, or service.

Any application or functionality described herein may be implemented as native code, by providing hooks into another application, by facilitating use of the mechanism as a plug-in, by linking to the mechanism, and the like.

Having described our invention, what we now claim is as follows.