Why Context Graphs Will Define AI Success in Regulated Industries
Regulated enterprises face a fundamental problem with AI: systems that can’t explain themselves. “The model said so” isn’t an acceptable answer: regulators need provenance, auditors want lineage and risk teams want to understand exactly why an AI system reached a particular conclusion.
This is where context graphs come in and why we believe they’ll become essential infrastructure for any enterprise serious about scaling trustworthy AI in regulated environments through 2026 and beyond.
The Evolution of Enterprise AI Architecture
The tooling for enterprise AI has matured rapidly. Most organisations now have some combination of RAG (retrieval-augmented generation), vector databases and increasingly, protocols like MCP (Model Context Protocol), that allow AI systems to access structured data from multiple sources in real-time.
These approaches have solved important problems. Vector databases make semantic search practical at scale, RAG grounds model outputs in actual enterprise content and MCP enables AI agents to pull context from live systems, including CRMs, databases and document stores, without bespoke integration for each source.
But there’s a gap.
These technologies excel at retrieving information, but what they don’t inherently capture is the relationship between that information: who created it, when it was approved, what policy it supersedes, or how it connects to your broader compliance framework.
When a regulator asks “what was your policy on X at the time of incident Y?”, a vector search gives you similar documents, or RAG retrieves relevant passages, or MCP pulls current state from connected systems. None of these alone give you the auditable reasoning chain that regulated industries increasingly require.
What Context Graphs Add to the Stack
Context graphs are knowledge structures that capture not just information, but the relationships, provenance and temporal context around that information. They’re not a replacement for RAG, vectors, or MCP, but a layer that sits alongside these technologies to provide the explainability and auditability that regulated environments demand.
For regulated industries, this translates to three critical capabilities.
Explainable reasoning chains. When an AI system makes a recommendation, a context graph can trace precisely which sources informed that conclusion, when those sources were last updated and how they relate to each other. This auditability will become increasingly important with new legislation like the EU AI Act evolving rapidly.
Temporal awareness. Context graphs can maintain records of historical states at various points in time, in ways that document stores and real-time protocols don’t address.
Permission-aware access. Most enterprises have complex governance structures around who can see what information. Context graphs can encode these permissions directly into the knowledge structure, ensuring AI systems only reason over information the user is authorised to access.
Where We See This Heading Across Sectors
Context graphs are still an emerging pattern in enterprise AI architecture. The technology isn’t new (graph databases have existed for decades), but their application to AI explainability and compliance is relatively early stage. That said, the use cases are becoming clearer and we expect adoption to accelerate as regulatory pressure increases.
In financial services, we anticipate context graphs enabling compliance systems that can trace regulatory interpretations back to source guidance, link those to internal policies and connect policies to the specific procedures that implement them. When the FCA asks how you’re meeting Consumer Duty requirements, you wouldn’t be searching through documents - you’d traverse a graph that shows exactly how high-level obligations flow through to customer-facing processes.
In energy and utilities, the potential lies in asset management and safety case documentation. An engineer asking about maintenance requirements for a specific asset type could receive answers that incorporate manufacturer specifications, regulatory requirements, historical maintenance records and incident reports, with clear lineage showing where each piece of information originated and when it was last validated.
In manufacturing, quality control and supply chain compliance stand to benefit significantly. Demonstrating that a product meets regulatory specifications could mean connecting design requirements to test results to supplier certifications to production records. Auditors would be guaranteed to get a defensible chain of evidence.
The Build Considerations
If you’re evaluating context graphs for your AI roadmap, there are realities to acknowledge.
Context graphs require investment in knowledge engineering. Unlike vector databases, where you can point at a document store and start indexing, context graphs need intentional design. Someone needs to define the entity types, relationship structures and provenance requirements that matter for your compliance context.
Integration with existing infrastructure matters. Your context graph needs to complement your existing RAG pipelines, vector stores and MCP connections, not replace them. The goal is to add a layer of relationship and provenance tracking that these technologies don’t provide natively.
Start small. We’d typically advise focusing on a specific compliance domain, such as a particular regulation, a specific asset class, or a defined operational process, rather than attempting an enterprise-wide graph from day one. Prove value in a contained area, then expand.
The Regulatory Direction
The EU AI Act’s requirements for high-risk systems essentially mandate the kind of explainability that context graphs enable. Financial services regulators are increasingly asking not just “what did you decide”, but “how did you decide it and can you prove it?” Energy regulators expect demonstrable safety cases. Manufacturing quality standards require traceable compliance evidence.
This regulatory trajectory suggests that enterprises building AI systems today should be thinking about explainability infrastructure now, even if full implementation is a 2026 or 2027 milestone.
Closing Thoughts
Context graphs aren’t a silver bullet and they’re not yet a mature, off-the-shelf capability. But for regulated industries where auditability, provenance and explainability aren’t optional, they represent an important evolution in how we architect AI systems.
The enterprises that start building this capability, even in limited, focused ways, will be better positioned as regulatory requirements tighten and AI systems take on more consequential decisions. Context graphs force rigorous thinking about knowledge provenance and relationship modelling. That discipline pays dividends well beyond satisfying auditors.
If you’re planning your AI architecture for regulated environments, context graphs should be part of the conversation - not necessarily as immediate implementation, but as a direction of travel for trustworthy, auditable AI.
