Identity Collection Auditing for Least-Privilege Access Control
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Solution Overview
Problem
Managing identity collections to ensure that appropriate identities belong to each collection, especially as the size of the collection increases or decreases, is challenging, as it affects access to computing resources and security.
Innovation Solution
A system using an identity collection membership model that enforces constraints and tunable parameters to audit and adjust membership based on the number of resources and members, providing visual comparisons and recommendations for inclusion or exclusion of identities, and continuously learning through user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If identity collection size increases to improve resource access coverage, then more identities can access computing resources, but security risks increase and management complexity increases
Solution Approach 1:
The system performs preliminary actions by continuously auditing identity collection membership before security issues arise. It proactively identifies inappropriate members and recommends exclusions, preventing security risks rather than reacting to them after incidents occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring identity collection compositions and providing recommendations for adjustments. This closed-loop approach ensures that as collections grow, the system automatically identifies and flags potential security issues, maintaining security posture despite increased size and access coverage.
2Adaptability or versatility
If identity collection size increases to improve resource access coverage, then more identities can access computing resources, but management complexity increases
Solution Approach 1:
The system enables self-service by automatically auditing identity collections and generating membership recommendations without requiring manual intervention. The automated analysis and recommendation generation reduce the burden on administrators, allowing them to manage larger collections with less effort.
Solution Approach 2:
The system manages complexity by changing key parameters such as collection sizes and membership compositions based on audit results. By systematically adjusting these parameters through automated recommendations, the system maintains optimal configurations even as collections grow in size and complexity.
3Reliability
If manual auditing methods are used to ensure appropriate identity membership, then security can be maintained, but time consumption increases and scalability decreases
Solution Approach 1:
The system replaces manual mechanical auditing processes with automated computational methods. Instead of administrators manually reviewing each identity collection, the system automatically analyzes compositions, identifies inappropriate members, and generates recommendations, dramatically reducing time consumption while maintaining security assurance.
Solution Approach 2:
The system introduces an intermediary automated auditing layer between manual security processes and identity collections. This intermediary automatically performs the time-consuming analysis and recommendation generation, allowing security to be maintained without requiring significant administrator time investment.
4Reliability
If strict membership constraints are enforced to improve security, then security risks are reduced, but resource utilization decreases
Solution Approach 1:
The system applies partial action by implementing security constraints selectively rather than universally. It identifies specific inappropriate members for exclusion while allowing appropriate members to remain, achieving security improvements without unnecessarily restricting resource access and utilization.
Solution Approach 2:
The system applies local quality by tailoring membership constraints to specific identity collections and their contexts. Rather than enforcing uniform strict constraints across all collections, it applies appropriate security measures locally to each collection based on its specific members and resource access patterns, maintaining security while preserving resource utilization.
Data Source
AI summary
Techniques for identity collection (IC) membership auditing include: determining, based on IC-to-identity mappings, a number of members of a particular IC; determining, based on IC-to-resource mappings, a number of computing resources accessible to the particular IC; determining one or more parameter values for an IC membership model that defines an inverse relationship between a number of computing resources accessible to a given IC and an expected number of members of the given IC; applying the number of computing resources accessible to the particular IC and the one or more parameter values to the IC membership model, to obtain an expected number of members of the particular IC; and responsive to determining that the number of members of the particular IC exceeds the expected number of members of the particular IC, placing the particular IC under administrative review.