System and method for peer group detection, visualization and analysis in identity management artificial intelligence systems using cluster based analysis of network identity graphs
Systems and methods for graph based artificial intelligence systems for identity management systems are disclosed. Embodiments of the identity management systems disclosed herein may utilize a network graph approach to peer grouping of identities of distributed networked enterprise computing environment. Specifically, in certain embodiments, data on the identities and the respective entitlements assigned to each identity as utilized in an enterprise computer environment may be obtained by an identity management system. A network identity graph may be constructed using the identity and entitlement data. The identity graph can then be clustered into peer groups of identities. The peer groups of identities may be used by the identity management system and users thereof in risk assessment or other identity management tasks.
1. An identity management system, comprising:
a graph data store;
a processor;
a non-transitory, computer-readable storage medium including computer instructions for:
obtaining identity management data from one or more identity management systems in a distributed enterprise computing environment, the identity management data comprising data on a set of identities and a set of entitlements associated with the set of identities utilized in identity management in the distributed enterprise computing environment;
evaluating the identity management data to determine the set of identities and a set of entitlements associated with the set of identities;
generating a first identity graph from the identity management data by:
creating a node of the first identity graph for each of the determined set of identities,
for each first identity and second identity that share at least one entitlement of the set of entitlements, creating an edge of the first identity graph between a first node representing the first identity and a second node of the identity graph representing the second identity, and
generating a similarity weight for each edge of the first identity graph between each first node and second node based on a number of the set of entitlements shared between the first identity represented by the first node and the second identity represented by the second node;
storing the first identity graph in the graph data store;
pruning the set of edges of the first identity graph to generate a second identity graph based on the similarity weight associated with each edge of the first identity graph and a pruning threshold;
storing the second identity graph in the graph data store;
clustering the set of identities represented by the nodes of the second identity graph into a set of peer groups based on the second identity graph, including the nodes of the second identity graph representing the set of identities, the edges of the second identity graph and the similarity weights of each of the edges of the second identity graph;
associating each of the set of identities with a corresponding peer group; and
generating an interface based on the second identity graph and the association between each of the set of identities and the corresponding peer group.
2. The system of claim 1 , wherein the instructions are further for:
determining a peer group assessment metric based on the set of peer groups and comparing the peer group assessment metric to a quality threshold, and
when the peer group assessment metric is below the quality threshold:
a) adjusting the pruning threshold;
b) pruning the set of edges of the first identity graph to generate the second identity graph based on the similarity weight associated with each edge of the first identity graph and the adjusted pruning threshold;
c) clustering the set of identities represented by the nodes of the second identity graph into a set of peer groups based on the second identity graph, including the nodes of the second identity graph representing the set of identities, the edges of the second identity graph and the similarity weights of each of the edges of the second identity graph;
d) determining the peer group assessment metric and comparing the peer group assessment metric to the quality threshold; and
e) repeating steps a-d until the peer group assessment metric exceeds the quality threshold.
3. The system of claim 2 , wherein the instructions are further for adjusting the pruning threshold a first amount a first time steps a-d are performed and adjusting the pruning threshold a second amount a second time steps a-d are performed.
4. The system of claim 3 , wherein the pruning threshold is adjusted up or down.
5. The system of claim 1 , wherein clustering the second identity graph is performed using Louvain community detection.
6. The system of claim 1 , wherein the peer group assessment metric is a modularity value resulting from clustering of the set of identities.
7. The system of claim 1 , wherein the peer group assessment metric is a user specified criteria.
8. A method, comprising:
obtaining identity management data from one or more identity management systems in a distributed enterprise computing environment, the identity management data comprising data on a set of identities and a set of entitlements associated with the set of identities utilized in identity management in the distributed enterprise computing environment;
evaluating the identity management data to determine the set of identities and a set of entitlements associated with the set of identities;
generating a first identity graph from the identity management data by:
creating a node of the first identity graph for each of the determined set of identities,
for each first identity and second identity that share at least one entitlement of the set of entitlements, creating an edge of the first identity graph between a first node representing the first identity and a second node of the identity graph representing the second identity, and
generating a similarity weight for each edge of the first identity graph between each first node and second node based on a number of the set of entitlements shared between the first identity represented by the first node and the second identity represented by the second node;
storing the first identity graph in a graph data store;
pruning the set of edges of the first identity graph to generate a second identity graph based on the similarity weight associated with each edge of the first identity graph and a pruning threshold;
storing the second identity graph in the graph data store;
clustering the set of identities represented by the nodes of the second identity graph into a set of peer groups based on the second identity graph, including the nodes of the second identity graph representing the set of identities, the edges of the second identity graph and the similarity weights of each of the edges of the second identity graph;
associating each of the set of identities with a corresponding peer group; and
generating an interface based on the second identity graph and the association between each of the set of identities and the corresponding peer group.
9. The method of claim 8 , further comprising:
determining a peer group assessment metric based on the set of peer groups and comparing the peer group assessment metric to a quality threshold, and
when the peer group assessment metric is below the quality threshold:
a) adjusting the pruning threshold;
b) pruning the set of edges of the first identity graph to generate the second identity graph based on the similarity weight associated with each edge of the first identity graph and the adjusted pruning threshold;
c) clustering the set of identities represented by the nodes of the second identity graph into a set of peer groups based on the second identity graph, including the nodes of the second identity graph representing the set of identities, the edges of the second identity graph and the similarity weights of each of the edges of the second identity graph;
d) determining the peer group assessment metric and comparing the peer group assessment metric to the quality threshold; and
e) repeating steps a-d until the peer group assessment metric exceeds the quality threshold.
10. The method of claim 9 , further comprising adjusting the pruning threshold a first amount a first time steps a-d are performed and adjusting the pruning threshold a second amount a second time steps a-d are performed.
11. The method of claim 10 , wherein the pruning threshold is adjusted up or down.
12. The method of claim 8 , wherein clustering the second identity graph is performed using Louvain community detection.
13. The method of claim 8 , wherein the peer group assessment metric is a modularity value resulting from clustering of the set of identities.
14. The method of claim 8 , wherein the peer group assessment metric is a user specified criteria.
15. A non-transitory computer readable medium, comprising instructions for:
obtaining identity management data from one or more identity management systems in a distributed enterprise computing environment, the identity management data comprising data on a set of identities and a set of entitlements associated with the set of identities utilized in identity management in the distributed enterprise computing environment;
evaluating the identity management data to determine the set of identities and a set of entitlements associated with the set of identities;
generating a first identity graph from the identity management data by:
creating a node of the first identity graph for each of the determined set of identities,
for each first identity and second identity that share at least one entitlement of the set of entitlements, creating an edge of the first identity graph between a first node representing the first identity and a second node of the identity graph representing the second identity, and
generating a similarity weight for each edge of the first identity graph between each first node and second node based on a number of the set of entitlements shared between the first identity represented by the first node and the second identity represented by the second node;
storing the first identity graph in a graph data store;
pruning the set of edges of the first identity graph to generate a second identity graph based on the similarity weight associated with each edge of the first identity graph and a pruning threshold;
storing the second identity graph in the graph data store;
clustering the set of identities represented by the nodes of the second identity graph into a set of peer groups based on the second identity graph, including the nodes of the second identity graph representing the set of identities, the edges of the second identity graph and the similarity weights of each of the edges of the second identity graph;
associating each of the set of identities with a corresponding peer group; and
generating an interface based on the second identity graph and the association between each of the set of identities and the corresponding peer group.
16. The non-transitory computer readable medium of claim 15 , wherein the instructions are further for:
determining a peer group assessment metric based on the set of peer groups and comparing the peer group assessment metric to a quality threshold, and
when the peer group assessment metric is below the quality threshold:
a) adjusting the pruning threshold;
b) pruning the set of edges of the first identity graph to generate the second identity graph based on the similarity weight associated with each edge of the first identity graph and the adjusted pruning threshold;
c) clustering the set of identities represented by the nodes of the second identity graph into a set of peer groups based on the second identity graph, including the nodes of the second identity graph representing the set of identities, the edges of the second identity graph and the similarity weights of each of the edges of the second identity graph;
d) determining the peer group assessment metric and comparing the peer group assessment metric to the quality threshold; and
e) repeating steps a-d until the peer group assessment metric exceeds the quality threshold.
17. The non-transitory computer readable medium of claim 16 , wherein the instructions are further for adjusting the pruning threshold a first amount a first time steps a-d are performed and adjusting the pruning threshold a second amount a second time steps a-d are performed.
18. The non-transitory computer readable medium of claim 17 , wherein the pruning threshold is adjusted up or down.
19. The non-transitory computer readable medium of claim 15 , wherein clustering the second identity graph is performed using Louvain community detection.
20. The non-transitory computer readable medium of claim 15 , wherein the peer group assessment metric is a modularity value resulting from clustering of the set of identities.
21. The non-transitory computer readable medium of claim 15 , wherein the peer group assessment metric is a user specified criteria.