Open Data Infrastructure
Context Graphs as Regulatory Evidence for AI Systems
How context graphs can connect prompts, data products, policies, owners, lineage, decisions, and human review into inspectable evidence.
A folder of policies does not explain why an AI system produced a specific answer with specific data.
Regulatory evidence needs relationships
The EU AI Act establishes obligations around risk management, data governance, technical documentation, record keeping, transparency, and human oversight for covered AI systems. NIST AI RMF similarly frames governance and measurement as ongoing practices.
Those obligations need evidence that connects the AI behavior to data, policy, ownership, lineage, and review. A context graph is a practical way to represent those relationships.
The graph connects data to decisions
A useful context graph links prompts, tools, data products, policy decisions, source datasets, transformations, owners, evaluation results, and human reviews. It should show not only what the system used, but which controls shaped the output.
W3C PROV gives a vocabulary for provenance. OpenLineage gives a model for job and dataset lineage. A context graph can extend those ideas into AI-specific context assembly and decision review.
Core idea: context graphs turn AI governance from document collection into relationship evidence.
Governance teams need inspection paths
Open Data Infrastructure should make context graph nodes resolvable. A reviewer should be able to start with an AI answer and inspect source data, lineage, policy decisions, data product owners, evaluation status, and human approvals.
For adjacent context, read context graphs for AI root cause analysis, context graphs for policy simulation, and data provenance and the EU AI Act.
What breaks first
- Evidence exists in separate logs, catalogs, and ticket systems with no shared identifiers.
- Policy decisions are recorded, but the data context behind the decision is missing.
- Human review approves outputs without seeing lineage or freshness evidence.
- Documentation says a control exists, but no graph connects the control to actual AI behavior.
Questions to ask
Ask which identifiers connect prompts, tools, data products, policies, and reviews. Ask whether a regulator, auditor, or internal governance team can inspect one answer without manually joining five systems.
Sources to start with
These primary sources anchor the technical claims in this guide.
- EU AI Act official text
- NIST AI Risk Management Framework
- W3C PROV overview
- OpenLineage object model documentation
AI governance gets real when the evidence graph can follow the answer back to its data.