Automated Compliance Control Mapping via NLP
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Solution Overview
Problem
Manual mapping of custom compliance controls to standard control frameworks is time-consuming, resource-intensive, and prone to errors, as compliance managers need to manually match hundreds of control descriptions to ensure compliance with industry standards.
Innovation Solution
An automated system using supervised natural language processing (NLP) machine learning (ML) models to map custom compliance controls or questions to corresponding reference compliance controls by comparing text-based features, reducing the need for manual intervention and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual mapping of custom compliance controls to standard control frameworks is performed, then compliance accuracy can be ensured through human judgment, but time consumption and resource requirements increase substantially
Solution Approach 1:
The patent introduces an automated mapping system that acts as an intermediary between custom compliance controls and standard control frameworks. This system uses natural language processing and machine learning models to automatically match controls, eliminating the need for manual human judgment while maintaining high accuracy through trained algorithms that learn from compliance data patterns.
Solution Approach 2:
The patent replaces the mechanical manual process of compliance mapping with an automated computational system. The manual human effort of reading and matching control descriptions is substituted with an automated mapping engine that uses NLP and ML to perform the same function faster and more consistently, reducing time consumption while maintaining reliability.
2Measurement precision
If manual mapping of custom compliance controls is performed, then detailed human review can identify correct correspondences, but resource intensity and error potential increase
Solution Approach 1:
The patent implements a self-service automated mapping system that performs control correspondence identification without requiring human intervention. The system uses trained machine learning models to automatically analyze control descriptions, identify patterns, and determine correct correspondences between custom and standard controls, eliminating manual review while maintaining precision through algorithmic consistency.
Solution Approach 2:
The patent changes the parameters of the mapping process from manual human analysis to automated computational analysis. By transforming the input control descriptions into structured data formats and applying machine learning algorithms, the system achieves precise identification of control correspondences while simplifying the overall process complexity through automation.
3Productivity
If automated mapping using NLP ML models is implemented, then time and resources are reduced by 70-80%, but system complexity increases
Solution Approach 1:
The patent segments the automated mapping system into distinct functional modules: an NLP module for processing control descriptions, an ML model for pattern recognition and matching, and a mapping engine for generating results. This segmentation allows each component to specialize in specific tasks, improving overall productivity while managing system complexity through modular design that enables independent development and maintenance of each segment.
Data Source
AI summary
Techniques are described herein that are capable of automatic mapping of a question or compliance controls associated with a compliance standard to compliance controls associated with another compliance standard. Reference controls having respective first subsets of text-based features are identified. A question having a second subset of the text-based features or custom controls having respective second subsets of the text-based features are identified. Scores for the respective reference controls are determined for the question or each custom control using a supervised natural language processing machine learning model based at least on the first subsets of the text-based features and the second subset(s) of the text-based features. A compliance map is generated by automatically mapping the question or each custom control to a respective subset of the reference controls using the supervised natural language processing machine learning model based at least on the scores.