Access Policy Grouping to Reduce Policy Bloat in Computer Systems
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Solution Overview
Problem
Distributed computer systems face significant challenges with 'policy bloat' due to redundant and duplicative access policies, leading to performance issues, increased login times, and resource consumption, which existing manual optimization methods are reluctant to address.
Innovation Solution
An access policy optimization system (APOS) analyzes access policies, identifies equivalent policies among users, converts them to access groups, and removes redundant policies, using an access control health metric to trigger optimization when necessary, thereby reducing the number of policies while preserving access rights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual optimization methods are used to manage access policies, then administrators can maintain control over policy creation and modification, but the system experiences performance degradation, increased login times, and resource consumption due to policy bloat
Solution Approach 1:
The system performs self-service by automatically detecting redundant access policies and converting them to access groups without requiring continuous manual intervention. The policy optimization engine continuously monitors and transforms individual policies into grouped policies autonomously, reducing the burden on administrators while maintaining security.
Solution Approach 2:
The system changes the structural parameters of access policies by transforming individual user policies into grouped policies. This parameter change reduces the complexity and数量 of policies from thousands of individual entries to a manageable number of groups, directly improving system performance while preserving access control integrity.
2Adaptability or versatility
If the number of access policies is increased to accommodate more users and resources, then access control coverage is improved, but the system experiences increased login times and resource consumption
Solution Approach 1:
The system merges multiple individual access policies that grant the same or overlapping permissions to different users into a single access group. This combining process maintains comprehensive access control coverage for all users while reducing the total number of policy evaluations required during authentication, thereby decreasing login times.
Solution Approach 2:
Access groups serve as universal containers that can represent multiple individual policies simultaneously. A single access group can provide multi-functional access control for numerous users with similar permission requirements, eliminating the need to evaluate each individual policy separately and reducing authentication overhead.
3Manufacturing precision
If administrators manually review and optimize access policies, then policy accuracy can be maintained, but the optimization process cannot be performed frequently due to the time and effort required
Solution Approach 1:
The system replaces the mechanical manual review process with an automated policy optimization engine that uses algorithms to detect redundant policies and generate access groups. This substitution maintains policy accuracy through systematic analysis while enabling frequent optimization runs without requiring administrator time and effort for each execution.
Solution Approach 2:
The system implements feedback mechanisms where the policy optimization engine continuously monitors the access policy landscape, detects changes, and automatically triggers optimization processes when needed. This feedback loop ensures policy accuracy is maintained through regular automated reviews while adapting to changing system conditions without manual intervention.
Data Source
AI summary
Disclosed embodiments provide systems and methods for analyzing and optimizing access policies. Access policies are analyzed by an access policy optimization system. In cases where large numbers of users have similar access privileges, the number of overall policies can be significantly reduced. An access control health metric is computed on an original set of access data as a measure of the current state of the access policies. It can be used as an indication that optimization of the access policies is warranted. The access data can include access policies and/or access groups. A policy subgroup mapping process is performed to identify subgroups of access policies. Subgroups with a number of entries exceeding a predetermined value are converted to access groups, the users that have those policies are added to the corresponding access groups, and the individual access policies are deleted. Duplicative and/or redundant policies are identified and removed.