AI agent governance and data context: what should IAM teams do?

TL;DR: Enterprises are moving beyond AI experimentation toward production use cases where agents approve transactions, generate reports, and trigger workflows, and Collibra’s post argues that trusted data context now determines whether those actions stay inside control boundaries. The real issue is governance, not AI novelty: if lineage, certification, and ownership are stale, the agent is effectively operating outside policy.

NHIMG editorial — based on content published by Collibra: Collibra named Databricks' 2026 Data Governance Partner of the Year

Questions worth separating out

Q: How should security teams govern AI actions that depend on business data context?

A: They should treat context as an enforcement signal, not a description.

Q: Why do lineage and certification matter for production AI?

A: Because they tell the system whether the data behind the action is trustworthy for that use case.

Q: What do organisations get wrong about AI governance and data governance?

A: They separate them too early.

Practitioner guidance

• Map AI actions to governed data dependencies Identify which approvals, reports, and workflow triggers depend on certified datasets, business definitions, and lineage records.
• Block execution on stale context signals Prevent AI systems from using uncertified, outdated, or lineage-poor data for business actions.
• Unify ownership and classification across platforms Standardise business definitions, data ownership, quality certification, and regulatory classification so the same asset carries consistent meaning across analytics and AI environments.

What's in the full article

Collibra's full blog post covers the partnership and platform context this post intentionally leaves at a higher level:

• How Databricks Unity Catalog and Collibra divide governance responsibilities across data and AI assets
• The specific business context signals Collibra says should travel with data into production AI workflows
• Why the partnership matters for teams building governed paths from raw data to deployed agents
• The operational framing behind Collibra's Data Confidence message for enterprise data teams

👉 Read Collibra's post on why data governance is central to production AI →

AI agent governance and data context: what should IAM teams do?

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