AI Security Policy Refactoring for Rule Set Complexity
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Solution Overview
Problem
The complexity of security policies and their rule sets increases over time, leading to management challenges, increased computational resources, and higher administrator error rates due to their complexity.
Innovation Solution
A system and method that utilize an AI engine with a large language model (LLM) to refactor security policies by converting them into a code representation, allowing for analysis and optimization, including identifying inefficiencies, security gaps, and suggesting restructuring for improved performance and clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated agents continuously modify security policies to respond to security events, then security response capability is improved, but rule set complexity increases
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between automated security agents and the security policy rule sets. This intermediary automatically refactors and optimizes the rule sets, maintaining security response capabilities while managing complexity through systematic reorganization and consolidation of rules.
2Adaptability or versatility
If rule sets are expanded to cover more security scenarios, then security coverage is improved, but computational resources required for matching increase
Solution Approach 1:
The patent applies parameter changes by systematically optimizing rule set parameters such as ordering, grouping, and structure. The refactoring process reorganizes rules to improve matching efficiency, reducing computational resources while maintaining comprehensive security coverage through better-structured rule sets.
3Adaptability or versatility
If rule sets are expanded to cover more security scenarios, then security coverage is improved, but storage requirements increase
Solution Approach 1:
The patent employs merging by consolidating redundant or overlapping security rules into unified rules. The refactoring process identifies and combines duplicate rules, merging similar conditions and actions, thereby reducing storage requirements while preserving comprehensive security coverage through optimized rule consolidation.
4Reliability
If rule sets become more complex over time, then security response capability is improved, but administrator management burden increases
Solution Approach 1:
The patent implements self-service by enabling the security system to automatically refactor and optimize its own rule sets without administrator intervention. The automated refactoring process continuously manages rule set complexity, allowing the system to maintain high security response capability while reducing the administrative burden of manual policy management.
Data Source
AI summary
A computer device (referred to as a processing engine), system, and method are provided for refactoring an original security policy using an artificial intelligence (AI) engine including a large language model (LLM). The processing engine parses policy data converts the original security policy into a code representation and sends the converted code representation to the AI engine. The AI engine analyzes the original security policy by applying the LLM to the code representation and identifies policy insights that are sent to the processing engine.