Permission Administration Engine for Least-Privilege Access
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Solution Overview
Problem
Managing user permissions in complex computing environments is challenging, especially when users' roles change, leading to residual permissions that violate the principle of least privilege and pose security risks.
Innovation Solution
A system that automatically analyzes user interactions to identify permission structures, updates permissions dynamically, and notifies administrators, using machine learning to group similar permissions and enforce access controls without disrupting system access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user permissions are manually managed in complex computing environments, then administrators can control access, but residual permissions accumulate when user roles change, violating the principle of least privilege and creating security risks
Solution Approach 1:
The system automatically analyzes user interaction logs and permission data to identify residual permissions and generate updates without continuous manual intervention. The permission administration engine self-updates permission structures based on detected usage patterns, allowing the system to maintain itself autonomously while adhering to the principle of least privilege
Solution Approach 2:
The system continuously monitors user interactions and feeds this information back to the permission administration engine. This feedback loop enables the system to detect when permissions are no longer needed and automatically update permission structures, creating a dynamic adaptation mechanism that maintains security while reducing manual management overhead
2Reliability
If permissions are updated dynamically to enforce least privilege, then security is improved, but system access may be disrupted
Solution Approach 1:
The system performs preliminary analysis of permission structures and user interactions before implementing updates. By pre-identifying residual permissions and planning updates in advance, the system can make controlled changes that maintain security while minimizing disruption to system access and user productivity
Solution Approach 2:
The permission structure is made dynamic rather than static, allowing automatic adjustment based on detected usage patterns. The system adapts permissions in real-time based on actual user behavior, enabling security improvements without rigid constraints that would disrupt system access, as updates are driven by actual usage data rather than predetermined rules
3Reliability
If automatic permission analysis is implemented, then residual permissions are detected and security is improved, but device complexity increases
Solution Approach 1:
The permission administration engine performs multiple functions within a single system component: it analyzes user interactions, detects residual permissions, generates permission updates, and manages the update process. This multi-functional approach consolidates what would otherwise require multiple separate systems, reducing overall complexity while maintaining comprehensive security analysis and automatic permission management
Data Source
AI summary
A plurality of permissions associated with the on-demand computing services environment may be identified. Each of the permissions may identify a respective one or more actions permitted to be performed within the on-demand computing services environment. Each of the permissions may be granted to a respective one or more user accounts within the on-demand computing services environment. A degree of overlap between a first group of the user accounts granted a first one of the permissions and a second group of the user accounts granted a second one of the permissions may be determined. When the degree of overlap exceeds a designated threshold, a designated permission set that includes the first permission and the second permission may be created.