AI Security Policy Generation With Conflict Detection
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Solution Overview
Problem
Existing computerized security platforms require users to have specific knowledge of their unique computer language, syntax, and conventions to define security policies, leading to a steep learning curve and potential errors or conflicts.
Innovation Solution
A generative artificial intelligence system generates security policies directly compatible with the platform using natural language input, automatically detects conflicts, and facilitates role-based collaboration among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually define security policies using platform-specific computer language and syntax, then security policies can be precisely controlled, but users face a steep learning curve and high complexity in operation
Solution Approach 1:
The patent introduces an intermediary translation layer that converts natural language security policy descriptions into platform-specific configuration code. This mediator system includes language models trained on security policy data that automatically translate between human-readable natural language and machine-readable platform syntax, eliminating the need for users to learn complex platform-specific languages while maintaining precise policy control.
2Productivity
If users manually create security policies without formal verification, then policy creation is faster and simpler, but conflicts and errors in policies increase
Solution Approach 1:
The patent implements preliminary formal verification analysis that automatically checks security policies for conflicts and errors before they are deployed to the security platform. The system performs syntax validation, semantic consistency checks, and conflict detection algorithms that analyze policy relationships in advance, identifying potential issues before they cause problems in production environments.
Solution Approach 2:
The system provides automated feedback mechanisms that analyze created policies for potential conflicts and suggest corrections. The verification system returns detailed reports on policy consistency, highlighting specific conflicts and providing recommendations for resolution, allowing users to quickly iterate and improve policy quality without manual review of complex platform documentation.
3Device complexity
If security policies are generated without automated conflict detection, then the generation process is simpler and faster, but unpredictable or undesired behavior occurs in the security platform
Solution Approach 1:
The patent replaces manual mechanical review processes with automated computational verification systems. Machine learning models and formal verification algorithms automatically analyze policy conflicts, substitute human review with computational analysis that can systematically evaluate thousands of policy interactions without fatigue or error, ensuring consistent and predictable security platform behavior.
Data Source
AI summary
In an example method, a computer system accessing first natural language user input representing a request to generate security policies for a computerized security platform, and generates the security policies using a computerized large language model (LLM). Generating the one or more security policies includes determining an identity of the computerized security platform, providing at least a portion of the first natural language user input and the identity of the computerized security platform to the LLM, and receiving, from the LLM, first output data representing the security policies. The first output data has a computer language syntax that is compatible with the computerized security platform. Further, the system causes the security policies to be presented to a user and to be stored on a computerized storage device.