Multidimensional Vectors for Identity Permission Analysis
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Solution Overview
Problem
Existing identity and permission management systems in computer systems become cumbersome and inefficient, especially in large or dynamically changing environments, as they fail to dynamically update identity groups and minimize the attack surface by creating least-privilege groups, leading to increased risks from compromised identities with excessive permissions.
Innovation Solution
The development of multidimensional vectors to analyze and visually display identity permissions, using machine learning techniques to group similar identities together, update privileges, and implement a least-privilege policy, thereby streamlining privilege management and enhancing system security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual grouping of identities is implemented, then identity management becomes more organized, but it becomes difficult to implement effectively for large systems with many identities
Solution Approach 1:
The system automatically performs identity grouping and permission analysis without requiring manual intervention. The machine learning model autonomously analyzes identity data, determines optimal groupings, and assigns permissions, allowing the system to serve itself in managing identities at scale.
Solution Approach 2:
The patent replaces manual mechanical grouping processes with automated machine learning algorithms. Instead of administrators manually organizing identities, the system uses computational models to automatically analyze identity patterns, create groupings, and manage permissions.
2Reliability
If existing permission management techniques are used, then access control is maintained, but they do not dynamically update identity groups as circumstances change
Solution Approach 1:
The system dynamically updates identity groupings and permissions based on changing circumstances. The machine learning model continuously analyzes identity data and automatically adjusts group assignments and permission levels as identities evolve, ensuring both reliable access control and adaptability to new conditions.
Solution Approach 2:
The system incorporates feedback loops where identity usage patterns and access behaviors are continuously monitored and fed back into the machine learning model. This feedback enables the system to learn from actual usage and automatically adjust permission assignments to maintain optimal security and accessibility.
3Productivity
If identities are granted extensive permissions, then they can function effectively, but the attack surface increases if identities are compromised
Solution Approach 1:
The system applies the principle of local quality by granting permissions specifically tailored to each identity's actual needs and usage patterns. Instead of blanket extensive permissions, the machine learning model analyzes individual identity behaviors and assigns only the necessary permissions, maintaining functionality while minimizing the attack surface for each identity.
Solution Approach 2:
The system dynamically changes permission parameters based on identity analysis results. The machine learning model adjusts permission levels, group assignments, and access scopes as parameters, optimizing the balance between identity functionality and security exposure by granting only the minimum necessary permissions.
Data Source
AI summary
Disclosed embodiments relate to systems and methods for multidimensional vectors for analyzing and visually displaying identity permissions. Techniques include identifying a plurality of identities, privileges used by the identities, and data associated with the identities, developing privilege vectors based on the identified information, and generating groupings of the identities based on the privilege vectors. Further techniques include generating a group score for an identity grouping, using the group score to determine if the grouping is a least privilege grouping, and updating the privileges of the identities within the grouping.