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22/06/2026 10:03 am
TL;DR: Enterprise context, policy guardrails, lineage, and runtime trust signals can follow data and agents into production as Collibra and Databricks expanded bi-directional governance across Unity Catalog, Genie, and Agent Bricks, according to Collibra. That matters because agentic AI fails quickly when metadata, access boundaries, and accountability are fragmented across platforms.
NHIMG editorial — what this means for AI and NHI governance
By the numbers:
- 60% of the Fortune 500 rely on Databricks, Databricks to build and scale data and AI apps, analytics and agents.
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, with 46% confirmed and 26% suspected.
Questions worth separating out
Q: How should security teams govern AI agents that rely on enterprise data context?
A: Security teams should treat business metadata, lineage, and data certification as part of the control surface, not just reference information.
Q: When does governance break down for agentic AI systems?
A: Governance breaks down when the system can still authenticate and access data but no longer has a reliable, current understanding of what that data means or who owns it.
Q: How do you know if AI agent trust controls are actually working?
A: Look for runtime evidence, not just policy approval.
Practitioner guidance
- Map governed context to decision paths Inventory where AI agents consume business definitions, ownership data, certifications, and lineage before they act.
- Separate static approval from runtime trust Use pass rates, lineage drift, and unmonitored-agent counts as operational indicators, then tie them to lifecycle review rather than relying on one-time approval alone.
- Validate agent access boundaries at the point of consumption Check that certified datasets, quality scores, and masking rules are enforced where agents query data, not only where data is catalogued.
What's in the full announcement
Collibra's full article covers the operational detail this post intentionally leaves for the source:
- Preview-specific integration points across Unity Catalog, Genie, Agent Bricks, and Collibra's AI Command Center.
- The live trust-score signals, pass-rate metrics, and agent monitoring details used in the Databricks workflow.
- How the bi-directional metadata and lineage exchange is configured for governance and traceability.
- What preview customers and Databricks marketplace users can validate in live demonstrations and implementation paths.
👉 Read Collibra's analysis of expanded governance for Databricks agentic AI →
Agentic AI governance in Databricks: what changes for IAM teams?
Explore further
22/06/2026 10:47 am
Agentic AI governance fails when context is treated as documentation instead of control. This partnership shows that business definitions, ownership, quality signals, and lineage now function as operational inputs to machine behaviour, not just reference material. Once an agent uses those inputs to choose an action, stale or incomplete context becomes a governance failure, not a data-quality annoyance. Practitioners should treat context governance as part of the authorisation surface.
A few things that frame the scale:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, with 46% confirmed and 26% suspected, according to The 2024 ESG Report: Managing Non-Human Identities.
- Enterprises that have experienced a compromised NHI averaged 2.7 separate incidents in the past 12 months, according to The 2024 ESG Report: Managing Non-Human Identities.
A question worth separating out:
Q: What is the difference between data governance and agent governance?
A: Data governance defines what data means, who owns it, and how it should be used. Agent governance extends that work into runtime by checking whether the AI system continues to use governed context correctly when it selects tools, interprets data, and triggers actions. In practice, the two need to be connected, not separated.
👉 Read our full editorial: Collibra and Databricks extend governance into agentic AI operations
