RAG, Agents and Graph: Your AI Compliance Dream Team
The landscape of AI governance is rapidly evolving, presenting organisations with unprecedented challenges in understanding and implementing regulatory requirements. As we witness the introduction of comprehensive frameworks such as the EU AI Act, with its 150+ pages of technical language and both broad and vague definitions, organisations are struggling to efficiently process and implement these complex regulatory demands. This is further compounded with other broad-sweeping regulations like the Digital Operational Resilience Act (DORA) and more formerly the General Data Protection Regulation (GDPR).
In short, compliance and governance is a hard undertaking for enterprise organisations and it represents one of the biggest non-discretionary balance sheet items for regulated businesses. In this blog, we will explore how the use of agents, Retrieval Augmented Generation (RAG) and Knowledge Graph capabilities can be unified to support organisations in driving down cost in this space, whilst laying common foundations that can be used to support their AI governance approaches, as well as a host of other intersecting regulatory obligations.
The Current State of Regulatory Compliance
Today's organisations typically manage regulatory compliance through resource-intensive manual processes. Teams spend weeks, if not months, reviewing both regulatory and technical documentation alongside their regular duties. This challenge is particularly acute when organisations face multiple regulatory frameworks, with most needing to analyse two to three major regulations annually across jurisdictions and entities.
The manual approach brings significant operational challenges. Compliance teams must meticulously review extensive documentation, cross-reference existing policies, and maintain consistency across interpretations and map these to their existing policies and controls. The real effort then comes in deciphering what is done today, versus what needs to be done tomorrow.
This process is further exacerbated when regulations are published in multiple languages, the complexity multiplies exponentially. Consequently, many organisations find themselves dependent on expensive consultancy engagements or by diverting resources that could be better allocated to enhancing controls and operational excellence.
The Limitations of Traditional Approaches
Current manual approaches to regulatory compliance reveal several critical weaknesses. Document review creates significant bottlenecks, with teams struggling to process extensive technical documentation efficiently. The challenge of maintaining consistency across different reviewers leads to varied interpretations of requirements. Version control becomes increasingly complex as regulatory updates accumulate, and the manual mapping between regulations, policies, and controls consumes valuable time while remaining prone to error.
These limitations often result in organisations taking a reactive rather than proactive approach to compliance, potentially missing crucial requirements or failing to identify important policy implications.
In the advent of AI, and more specifically, agents we think things can change and should change for the betterment of firms and the customers they serve.
A New Paradigm: The Multi-Agent Approach
To address these challenges, we have seen organisations reap benefits through the combination of sophisticated multi-agent systems, RAG databases and knowledge graphs. In unison, these capabilities can become a dream team for your organisations compliance posture. Whether that is the EU AI Act, DORA or GDPR for that instance.
This is specifically the case where specialised AI agents work collaboratively to analyse, interpret, and validate regulatory requirements and cross reference these against documented policies and controls that often underpin the control environments of many regulated enterprise organisations. This approach ensures clear separation of concerns while maintaining robust explainability throughout the process.
Consider this example team of specialist agents:
An Analysis Agent focuses solely on examining regulatory documents and breaking them down into discrete requirements. Either by chapter, theme or domain. This agent maintains a singular focus on understanding and categorising regulatory content, ensuring clear separation from the policy mapping process.
A Mapping Agent then takes these categorised requirements and correlates them with existing policies and controls. By keeping this process separate from the initial analysis, organisations maintain clear traceability and can more easily validate the reasoning behind each mapping decision.
A Review Agent provides an additional layer of validation by examining the work of both previous agents, identifying potential gaps or inconsistencies. This reflection pattern ensures that each step of the process can be independently verified and explained before passing off to a human for review.
The output of the analysis could be a report or some form of digital artefact which a Compliance or audit specialist can interpret without having to do the heavy lifting conducted by the agent. Or to that point, expensive 3rd party consulting companies who specialise in audit and assurance services.
The Technical Foundation
The effectiveness of this multi-agent approach rests on three crucial technological pillars:
Agentic RAG (Retrieval-Augmented Generation) Traditional document management systems often lack the context awareness needed for comprehensive regulatory understanding. By leveraging a RAG architecture, organisations can vectorise their regulations, controls and policies to create a dynamic, self-updating knowledge base that maintains precise citations and references. When regulatory changes occur, the system automatically identifies affected policies and controls, significantly reducing compliance gaps.
Knowledge Graph Implementation Understanding the complex web of relationships between regulations, policies, and controls presents a significant challenge. Traditional spreadsheet-based approaches quickly become unwieldy. By leveraging a knowledge graph implementation, like Neo4J or AWS Neptune, organisations can create a living map of these relationships, enabling organisations to visualise and understand the impact of regulatory changes across their entire compliance framework.
Enterprise Integration Framework Many organisations have made substantial investments in existing governance tools and document management systems. Our solution integrates with these investments through a flexible enterprise framework, automating information flow while maintaining comprehensive audit trails. This creates a seamless connection between regulatory requirements, policy documents, and control implementations.
The Role of Human Expertise
While AI agents significantly accelerate the compliance process, human expertise remains crucial for strategic oversight. Compliance professionals and subject matter experts provide essential validation of agent interpretations, resolve ambiguities, and make final approval decisions on policy changes.
This human-in-the-loop approach ensures that automation enhances rather than replaces expert judgment, allowing organisations to focus their valuable human resources on strategic decision-making rather than routine document processing.
Implementation Strategy
Organisations should approach this transformation through a measured, phased implementation:
The initial setup phase focuses on defining regulatory scope, configuring agent workflows, and establishing the foundational knowledge graph. This creates a solid base for the solution while minimising disruption to existing processes.
A focused pilot phase follows, typically concentrating on a single regulation. This allows organisations to validate agent outputs, refine workflows based on practical experience, and document effectiveness metrics before scaling the solution.
The scaling phase then expands the solution to additional regulations while continuously enhancing knowledge graph connections and automating routine updates based on learned experiences.
Building for the Future
This automated approach to regulatory compliance represents more than just an efficiency tool—it's a strategic necessity for organisations navigating the increasingly complex landscape of AI and the wider digital governance domain. By creating a living regulatory knowledge base that adapts automatically to new requirements, organisations can maintain historical context while enabling proactive compliance planning.
The multi-agent approach, with its clear separation of concerns and robust explainability, provides organisations with the tools they need to maintain compliance while driving innovation forward. As regulatory requirements continue to evolve, particularly in the AI space, this automated approach becomes increasingly valuable for organisations seeking to maintain effective governance while optimising resource allocation.
Conclusion
The future of regulatory compliance lies in the intelligent application of AI technologies, enabling organisations to stay ahead of requirements while maintaining the human oversight necessary for effective governance. By automating the heavy lifting of regulatory analysis through carefully designed multi-agent systems, imbued with RAG and graph capabilities, organisations can focus their expertise where it matters most—ensuring their technology implementations serve their intended purpose while maintaining the highest standards of governance and control.
