Systems and methods for access management

A computer-implemented method for access management using machine learning, the method comprising:

• obtaining user information and access right information from an access directory and a database;
• preprocessing the obtained user information and access right information into user access information, wherein the user access information comprises at least a combination of the user information and the access right information;
• generating, based on the user access information, a network for community determination comprising nodes and edges, wherein:
• the nodes comprise:
• a first type of nodes representing users based on the preprocessed user access information;
• and
• a second type of nodes representing access rights based on the preprocessed user access information;
• the edges comprise:
• a first type of edges connecting nodes of the first type and nodes of the second type, the first type of edges comprising a confidence parameter indicating a likelihood that users represented by nodes of the first type have access rights represented by nodes of the second type, wherein the confidence parameter is computed based on a ratio between a number of users within a prior result of a community determination that have that access right and a total number of users within the prior result of the community determination;
• and
• a second type of edges connecting two nodes of the first type and indicating degrees of similarity between two users represented by the two nodes, based on a set of access rights of the two users, wherein the degrees of similarity is based on the access right information;
• and
• determining, using an unsupervised machine learning algorithm, from the generated network for community determination, and from the user information, a group of nodes of the first type, the group representing a community of users sharing a degree of commonality higher than a degree of commonality shared by other users outside the community.

A non-transitory computer-readable medium storing instructions executable by at least one processor to cause the at least one processor to perform operations for access management using machine learning, the operations comprising:

• obtaining user information and access right information from an access directory and a database;
• preprocessing the obtained user information and access right information into user access information, wherein the user access information comprises at least a combination of the user information and the access right information;
• generating, based on the user access information, a network for community determination comprising nodes and edges, wherein:
• the nodes comprise:
• a first type of nodes representing users based on the preprocessed user access information;
• and
• a second type of nodes representing access rights based on the preprocessed user access information;
• the edges comprise:
• a first type of edges connecting nodes of the first type and nodes of the second type, the first type of edges comprising a confidence parameter indicating a likelihood that users represented by nodes of the first type have access rights represented by nodes of the second type, wherein the confidence parameter is computed based on a ratio between a number of users within a prior result of a community determination that have that access right and a total number of users within the prior result of the community determination;
• and
• a second type of edges connecting two nodes of the first type and indicating degrees of similarity between two users represented by the two nodes, based on a set of access rights of the two users, wherein the degrees of similarity is based on the access right information;
• and
• determining, using an unsupervised machine learning algorithm, from the generated network for community determination, and from the user information, a group of nodes of the first type, the group representing a community of users sharing a degree of commonality higher than a degree of commonality shared by other users outside the community.