Regulatory Knowledge Graph: The Core of AI Compliance Intelligence

What is a Regulatory Knowledge Graph?

At the core of Veritas AI lies our proprietary Regulatory Knowledge Graph - a sophisticated network of interconnected regulatory concepts that represents the complex world of AI compliance in a structured, machine-readable format. Unlike traditional databases that store information in tables and rows, a knowledge graph uses a graph structure with nodes (entities) and edges (relationships) to represent knowledge in a way that mirrors how humans understand the world.

Our Regulatory Knowledge Graph is specifically designed to capture the intricate relationships between:

• Regulatory Frameworks: EU AI Act, GDPR, NIST AI RMF, ISO 42001, and other global AI governance frameworks
• Requirements: Specific compliance obligations derived from regulatory texts
• Controls: Measures and mechanisms that can satisfy multiple requirements
• Evidence Types: Documentation and artifacts that demonstrate compliance

Graph RAG: Beyond Traditional Retrieval-Augmented Generation

Our knowledge graph implements an advanced form of Retrieval-Augmented Generation (RAG) that we call Graph RAG. While traditional RAG systems retrieve information from unstructured text to enhance AI responses, our Graph RAG leverages the rich structure of the knowledge graph to provide more accurate and contextual compliance information.

How Graph RAG Works:

1. Query Transformation: Your compliance queries are transformed into graph traversal operations
2. Structural Retrieval: Rather than simple keyword matching, we identify relevant regulatory nodes and their multi-hop relationships
3. Contextual Understanding: The system extracts contextually relevant data by following connections through the graph
4. Evidence-Based Response: Compliance guidance is generated with supporting evidence from actual regulatory texts

Comprehensive Coverage of Global AI Regulations

Our knowledge graph currently encompasses:

This comprehensive coverage allows us to identify overlapping requirements across different frameworks, helping organizations streamline their compliance efforts by implementing controls that satisfy multiple regulatory requirements simultaneously.

Key Benefits of the Knowledge Graph Approach

1. Requirement De-duplication

Our knowledge graph identifies similar or overlapping requirements across different regulatory frameworks. This allows organizations to implement a single control that satisfies multiple compliance requirements, significantly reducing redundant work.

For example, both the EU AI Act and ISO 42001 require documentation of AI system development processes, but with slightly different emphases. Our system identifies these overlaps and suggests unified documentation approaches.

Learn more about requirement de-duplication

2. Cross-Framework Compliance Analysis

For organizations operating across multiple jurisdictions, our knowledge graph provides a consolidated view of all applicable requirements. This enables a unified compliance strategy rather than a siloed approach to each regulatory framework.

3. Efficient Evidence Collection

By mapping requirements to specific types of evidence, our system helps organizations understand exactly what documentation they need to produce. This prevents both over-documentation (creating unnecessary artifacts) and under-documentation (missing critical evidence).

4. Future-Proofing Compliance

As new regulations emerge or existing ones evolve, we update the knowledge graph to reflect these changes. This allows our system to automatically identify the impact on your compliance status and recommend necessary adjustments.

How to Use the Knowledge Graph Tool

Our interactive Knowledge Graph tool allows you to explore the connections between different regulatory frameworks, requirements, and compliance controls. Here's how you can get the most out of it:

The Technical Implementation

For those interested in the technical aspects, our knowledge graph is built on a specialized graph database optimized for regulatory intelligence. We employ natural language processing to extract requirements from regulatory texts and machine learning to identify conceptual similarities across frameworks.

The knowledge graph is continuously maintained and updated by our team of regulatory experts and data scientists, ensuring it remains accurate and current as the regulatory landscape evolves.

Knowledge Graph Access Across Subscription Tiers

Our knowledge graph capabilities are available across all subscription tiers, with increasing levels of functionality:

For more information on subscription options, please visit our pricing page.

Conclusion

The Regulatory Knowledge Graph is the foundation of Veritas AI's unique approach to AI compliance. By representing regulatory requirements as an interconnected graph rather than isolated rules, we provide a more intelligent, efficient, and adaptable solution for navigating the complex landscape of AI governance.

Whether you're just beginning your compliance journey or looking to streamline existing processes across multiple regulatory frameworks, our knowledge graph technology offers unparalleled insights and efficiencies that traditional compliance approaches cannot match.