Context-Aware Permission Reduction Using Criticality and Stability Scores
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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 due to balancing access needs with security risks.
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 resource characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If entities are granted excessive permissions to ensure they can perform their functions, then functional completeness 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 adjusted in real-time based on entity criticality scores, stability scores, and security gain scores calculated from historical interaction data.
Solution Approach 2:
The patent changes the parameter of permission scope from fixed to variable by introducing three key scoring parameters: entity criticality score (measuring importance), stability score (measuring usage consistency), and security gain score (measuring risk reduction). These parameters enable granular control over permission levels, allowing the system to optimize the balance between functionality and security.
2Object-affected harmful factors
If permission sets are reduced to minimize security risks, then security vulnerability is reduced, but functional completeness deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors entity interactions with resources, calculates stability scores based on historical usage patterns, and uses this feedback to inform permission adjustment decisions. The feedback loop ensures that permission reductions do not inadvertently break functional requirements, as the system learns from actual usage behavior.
Solution Approach 2:
The patent performs preliminary analysis by calculating entity criticality scores and stability scores before making permission adjustments. This preliminary assessment ensures that permission reductions are made safely, with the understanding of which entities require higher permissions and which can tolerate reduced access without impacting functionality.
3Adaptability or versatility
If manual permission management is used to balance security and functionality, then customization is improved, but operational complexity increases
Solution Approach 1:
The patent implements self-service permission management where the system automatically calculates scores, determines optimal permission sets, and applies adjustments without requiring manual intervention. The system serves itself by monitoring its own security posture and autonomously making permission decisions based on predefined scoring criteria, eliminating the need for complex manual management processes.
Solution Approach 2:
The patent creates a universal permission management framework that handles multiple entities, resources, and permission types through a single automated system. The multi-functional scoring mechanism evaluates criticality, stability, and security gain across diverse scenarios, providing a unified approach that replaces multiple manual processes while maintaining customization capabilities.
4Ease of operation
If automated permission adjustment is implemented to reduce operational complexity, then ease of operation is improved, but system complexity increases
Solution Approach 1:
The patent segments the permission management system into distinct functional modules: entity criticality assessment module, stability score calculation module, security gain evaluation module, and permission adjustment execution module. This segmentation allows each component to handle a specific aspect of the problem independently, making the overall complex system manageable through modular design while maintaining automated operation.
Data Source
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.