Open Data Infrastructure Governance Operating Models for AI

AI governance fails when every team owns a slice and nobody owns the path.

AI governance needs an operating model

Open Data Infrastructure governance for AI is not a committee name. It is the operating model that connects data product owners, platform teams, governance, AI builders, incident review, and portable control evidence.

The model has to define who owns data products, who owns policy, who owns the catalog, who owns AI tool access, who responds to failures, and which evidence proves that each responsibility was executed.

Govern, map, measure, and manage need owners

NIST AI RMF gives the govern, map, measure, and manage frame for AI risk management. ODI adds the data infrastructure spine: catalogs, table contracts, metadata, lineage, policy, access logs, evaluation evidence, and incident records.

The operating model should connect those layers with decision rights. A governance team can define policy, but platform teams need enforcement points. AI builders can design tools, but data owners need approval and escalation paths. Incident teams can respond, but evidence has to exist before the incident.

Patterns that work

• Name owners for data products, catalogs, policies, AI tools, evaluations, and incidents.
• Define which decisions require data owner approval before AI use.
• Create evidence packets for access, retrieval, tool use, data changes, and exceptions.
• Run incident reviews across the data path, not only the model path.
• Measure owner response and unresolved control gaps as operating metrics.

For adjacent ODI context, read AI workload control planes, data control-loop metrics, AI platform incident response.

What breaks first

• AI builders own tools, data teams own tables, and nobody owns the end-to-end context path.
• Governance approves policy but has no enforcement point in the catalog or query service.
• Incident response starts after the model output, not at source evidence.
• Control evidence exists in separate systems with no common identifiers.

Questions to ask

• Who can approve AI access to each data product?
• Which evidence packet proves the data path was allowed?
• Who responds when context is stale, denied, or wrong?
• Which metrics show that the operating model is working?

Sources to start with

These primary sources anchor the technical claims in this guide.

• NIST AI Risk Management Framework
• OpenLineage documentation
• Open Policy Agent decision logs
• DataHub documentation

The operating model is the part of AI governance that has to work on a Tuesday.