AI Compliance Mapping With Active Learning Across Rule Sets
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Solution Overview
Problem
Maintaining compliance with dynamically changing security requirements across different rule and regulation sets is challenging due to varying terms, languages, and sentence structures, leading to difficulties in balancing and understanding these requirements, which often necessitate significant manual effort and time.
Innovation Solution
An AI-supported system employs machine learning and deep learning to generate and update compliance control mappings, accounting for context-specific information and domain-specific dependencies, enabling efficient comparison and aggregation of compliance data across domains with differing semantics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to map compliance controls across different rule sets, then accuracy can be maintained through human judgment, but significant manual effort and time are required
Solution Approach 1:
The patent introduces an AI-based semantic mapping system as an intermediary between different compliance rule sets. The system uses trained language models to automatically translate and map controls between domains (e.g., NIST to ISO 27001), reducing manual effort while maintaining accuracy through contextual understanding and domain-specific training data.
Solution Approach 2:
The patent replaces manual mechanical mapping processes with automated AI-based semantic analysis. Machine learning models process and map compliance controls automatically, substituting human cognitive effort with computational processes that can handle large volumes of mapping tasks efficiently and consistently.
2Reliability
If comprehensive manual mapping of all compliance requirements is performed, then complete coverage is achieved, but the complexity and cost increase significantly
Solution Approach 1:
The patent segments the compliance mapping process into distinct AI model components: language understanding models, semantic relationship extractors, and domain-specific adapters. This modular architecture allows the system to handle complex mapping tasks through specialized subsystems, reducing overall system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent creates a universal AI-based mapping platform that can handle multiple compliance frameworks and domains simultaneously. The system uses generalizable language models trained on diverse compliance data, enabling a single system to serve multiple mapping needs without proportionally increasing complexity.
3Measurement precision
If domain-specific customization is applied to improve mapping accuracy for specific contexts, then context-specific precision improves, but the model complexity and training requirements increase
Solution Approach 1:
The patent applies local quality by training domain-specific adapters and fine-tuning parameters for specific compliance contexts while keeping the base language model generalizable. Each domain (e.g., healthcare, finance) receives customized processing through targeted training data and domain-specific vocabulary, improving precision without requiring complete model rearchitecture.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to compliance mapping, and more particularly to aggregated mapping of one or more sets of context-based compliance data with standard compliance data, such as from a target domain and one or more associate domains. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a mapping component that can map a compliance control for a target domain based on a model trained by an active learning process that incorporates a plurality of contexts representing relationships between entities and associate domain specific dependencies.