Access Management Using User Community Detection
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Solution Overview
Problem
Traditional access management methods rely heavily on organizational hierarchy, which becomes inefficient as users collaborate across multiple teams, leading to difficulties in identifying and managing access rights effectively, especially in large organizations with thousands of users.
Innovation Solution
A machine learning-based system that generates a network of users and access rights, using unsupervised algorithms like the Louvain Method to detect communities of users with similar access rights, allowing for the identification of anomalous access rights and recommendations for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional access management methods relying on organizational hierarchy are used, then access rights are easily assigned based on team structure, but the method becomes inefficient and inaccurate as users collaborate across multiple teams
Solution Approach 1:
The patent segments users into communities based on their access rights patterns rather than organizational hierarchy. By analyzing the commonality of access rights across users and grouping them into communities with similar access profiles, the system enables precise access rights assignment that reflects actual resource usage patterns rather than rigid team structures.
2Measurement precision
If manual auditing of access rights is performed on a user-by-user basis, then accurate assessment of necessary access rights is possible, but significant resources and time are required
Solution Approach 1:
The patent merges individual access rights assessments by identifying communities of users with similar access patterns. Instead of evaluating each user independently, the system combines information across users in the same community, using the access rights of one user to inform the access rights of others in the same community, thereby dramatically reducing the resources required while maintaining accuracy.
Solution Approach 2:
The system implements feedback mechanisms where access rights assignments are continuously analyzed and refined. By monitoring access patterns and community formations, the system provides feedback that improves access rights management over time, automatically adjusting assignments based on observed usage patterns and community characteristics.
3Ease of operation
If users keep access rights from previous projects, then access management is simple and fast, but security risks increase due to excess access rights
Solution Approach 1:
The patent dynamically changes access rights parameters based on community analysis and project requirements. Instead of static access rights that persist across projects, the system adjusts access rights parameters according to the user's current community membership and project needs, automatically revoking excess access while maintaining necessary permissions.
Data Source
AI summary
Systems and methods are provided for access management using machine learning. An exemplary system may include at least one processor and a storage medium storing instructions that, when executed by the processor, cause the processor to perform operations. The operations may include obtaining user access information from an access directory and generating a network comprising nodes and edges. The nodes may include a first type of nodes representing users and a second type of nodes representing access rights. The edges may include a first type of edges indicating that users have access rights, and a second type of edges indicating degrees of similarity between two users. The operations may also include determining a group of nodes of the first type representing a community of users sharing a degree of commonality higher than a degree of commonality shared by other users outside the community.