Natural Language Policy Extraction for Access Control
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Solution Overview
Problem
Manual creation of rule-based policies from natural language documents in physical access control systems is costly and impractical due to the time-intensive and error-prone process of transitioning to dynamic processing systems.
Innovation Solution
A system and method utilizing a natural language processor to analyze unstructured policy entries from a policy document database, generating formal policies that are then transformed into enforceable policies by a rule processor, with optional security domain knowledge, flowchart knowledge, and access control system compatibility databases to ensure accurate and efficient policy generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual creation of rule-based policies is used, then policy accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces a natural language processing system as an intermediary between unstructured policy documents and enforceable rule-based policies. This intermediary automatically analyzes, extracts, and transforms policy information, maintaining accuracy while dramatically reducing the time and manual effort required compared to completely manual creation.
Solution Approach 2:
The patent replaces the mechanical manual process of policy creation with an automated computational system. The natural language processor uses algorithms and machine learning to perform tasks that would otherwise require manual analysis and rule formulation, substituting human cognitive work with automated processing while maintaining policy quality.
2Reliability
If manual creation of rule-based policies is used, then policy quality can be ensured, but cost and effort increase
Solution Approach 1:
The system enables self-service policy generation by automatically processing unstructured policy documents and generating enforceable rules without requiring extensive manual intervention. The natural language processing system performs self-analysis and self-transformation of policy content, reducing the effort and expertise required for policy creation while maintaining quality through automated validation.
3Adaptability or versatility
If transition to dynamic processing system is made, then system adaptability improves, but implementation complexity increases
Solution Approach 1:
The patent segments the complex task of policy creation into distinct automated components: natural language processing, information extraction, rule generation, and validation. This segmentation allows the system to handle dynamic processing requirements through modular, manageable functions, reducing implementation complexity while improving adaptability to different policy scenarios.
Data Source
AI summary
A system and method for generating at least one policy includes a policy document database containing at least one policy document containing at least one unstructured policy entry, and a natural language processor to analyze the at least one unstructured policy entry to generate least one formal policy, wherein a formal outcome of execution of the at least one formal policy corresponds to the at least one unstructured policy entry, and a rule processor to transform the at least one formal policy entry to generate at least one enforceable policy, wherein an enforcement outcome of execution of the at least one enforceable policy corresponds to the at least one formal policy entry.