Context-Aware Permission Reduction for Least-Privilege Access
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Solution Overview
Problem
Existing systems often grant entities excessive permissions, increasing vulnerability to security threats, and achieving the principle of least privilege is complex and time-consuming due to balancing security and functionality needs.
Innovation Solution
A context-aware permission reduction system determines a candidate permission set based on criticality, stability, and security gain scores, using machine learning models to automatically adjust permissions based on historical interactions and entity characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If entities are granted excessive permissions to ensure functionality, then operational capability is improved, but security vulnerability increases
Solution Approach 1:
The patent implements dynamic permission adjustment by continuously monitoring entity behavior and automatically modifying permission sets based on observed patterns. The system transitions from static permission assignment to dynamic adaptation, where permissions are granted or revoked in real-time based on actual usage needs, thereby maintaining operational capability while reducing security vulnerabilities.
Solution Approach 2:
The system changes the parameter of permission scope by analyzing historical interaction data and adjusting the breadth of access rights. By transforming permission sets from fixed broad access to precisely scoped access based on actual usage patterns, the system maintains necessary operational capability while minimizing exposed security surfaces.
2Object-affected harmful factors
If permission reduction is implemented to improve security, then security risk is reduced, but system complexity increases due to balancing security and functionality
Solution Approach 1:
The patent implements self-service permission management where the system automatically analyzes entity behavior patterns and adjusts permissions without human intervention. The automated monitoring and adjustment mechanisms eliminate the need for manual security audits and permission reviews, reducing operational complexity while maintaining security improvements.
Solution Approach 2:
The system incorporates continuous feedback loops where entity interactions are monitored, analyzed, and used to automatically adjust permissions. This closed-loop approach simplifies permission management by using empirical data from actual usage rather than requiring complex manual security policies, thereby reducing system complexity while maintaining security effectiveness.
3Reliability
If manual permission management is used to maintain control, then security oversight is improved, but time consumption increases
Solution Approach 1:
The patent replaces manual mechanical permission management processes with automated computational systems. Machine learning models and behavioral analysis algorithms automatically perform security oversight functions that previously required manual review, thereby maintaining reliable security monitoring while eliminating time-consuming manual interventions.
Solution Approach 2:
The system performs preliminary analysis of entity behavior patterns and proactively adjusts permissions before security issues arise. By continuously monitoring and pre-adjusting permissions based on observed usage patterns, the system maintains reliable security oversight without requiring reactive manual intervention, thereby reducing time consumption.
Data Source
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AI summary
Systems, methods, apparatuses, and program products are disclosed for context-aware permission reduction. A candidate permission set is determined for an entity. A current permission set of the entity is replaced with the candidate permission set based on a criticality score indicative of a criticality of the entity, a stability score indicative of a likelihood that usage of a current permission set by the entity will change in a predetermined period of time, and a security gain score indicative of an amount of security improvement achievable by replacing the current permission set with the candidate permission set. The stability score for the entity may be determined based on historical usage of the current permission set by the entity.