Regulatory Intelligence Mapping for Continuous AI Compliance Monitoring
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Solution Overview
Problem
Organizations face challenges in adhering to the evolving regulatory landscape for AI technologies, particularly in healthcare, due to complex regulatory frameworks such as the EU AI Act, NIST AI risk management frameworks, ISO 42001, and other standards, leading to compliance obstacles and potential risks.
Innovation Solution
A system employing AI-driven horizon scanning and regulatory intelligence evaluation, using real-time source harvesting, sophisticated gap analysis, and customizable dashboards to monitor and align policies with industry standards, providing proactive alerts and strategic recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If organizations manually monitor and analyze regulatory changes, then they can understand compliance requirements, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual regulatory monitoring with an AI-driven system that automatically harvests, analyzes, and maps regulatory changes. The system uses machine learning models to process regulatory documents, identify changes, and map them to product requirements, eliminating the need for manual analysis while maintaining high compliance accuracy.
Solution Approach 2:
The system performs self-service by automatically monitoring regulatory landscapes, updating its own knowledge base, and generating compliance insights without human intervention. The AI models continuously learn from new regulatory data and autonomously update product requirement mappings.
2Reliability
If organizations use comprehensive regulatory frameworks like EU AI Act and NIST frameworks, then they can ensure compliance, but the complexity increases
Solution Approach 1:
The patent segments complex regulatory frameworks into manageable components by mapping specific regulatory requirements to discrete product requirements. The system breaks down frameworks like EU AI Act and NIST into actionable items that can be individually tracked and managed through the control matrix.
Solution Approach 2:
The system introduces an intermediary layer between regulatory frameworks and product implementations through the control matrix and AI-driven mapping mechanism. This intermediary translates complex regulatory language into actionable product requirements, reducing the perceived complexity while maintaining compliance assurance.
3Measurement precision
If organizations perform detailed gap analysis between current policies and industry standards, then they can identify compliance gaps, but the manual effort and time required increase
Solution Approach 1:
The patent replaces manual gap analysis with AI-driven automated comparison between current policies and industry standards. The system uses machine learning models to analyze policy documents, compare them against regulatory frameworks, and automatically identify gaps, eliminating manual analysis while maintaining high precision.
Solution Approach 2:
The system provides continuous gap analysis by automatically monitoring regulatory changes and continuously updating compliance assessments. The AI models continuously compare current policies against evolving standards, ensuring ongoing accuracy without requiring periodic manual re-assessment.
4Reliability
If organizations implement continuous monitoring of regulatory changes, then they can stay ahead of compliance updates, but the system complexity and resource requirements increase
Solution Approach 1:
The patent implements continuous monitoring through AI-driven automated systems that continuously harvest regulatory data, analyze changes, and update compliance mappings without human intervention. The machine learning models continuously process new regulatory information and automatically adjust product requirement mappings.
Solution Approach 2:
The system performs preliminary action by proactively identifying regulatory changes before they become compliance issues. The AI models continuously scan for emerging regulations and trends, allowing organizations to prepare and adjust their policies in advance rather than reacting to changes after they occur.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a system of one or more computers located in one or more locations. The system includes: obtaining input data from one or more regulatory resources; analyzing, using a first set of models, the obtained input data to determine insights related to industry regulations; further analyzing, using a second set of models, overlap between the input data and a control matrix, wherein the control matrix summarizes existing regulations; based on the overlap, determining a score to represent a degree of the overlap. Based on the degree of the overlap, the system provides summary of recommended next steps.