Control Tower controls: what they mean for data governance teams

TL;DR: Manual spot-checking no longer scales for data governance, AI safety, or regulatory compliance, according to Collibra’s analysis of Control Tower. The shift to continuous, exception-based enforcement matters because static metadata and periodic review leave blind spots where failures persist unnoticed.

NHIMG editorial — what this means for NHI practitioners

By the numbers:

• 72% of organisations have experienced or suspect they have experienced a breach of non-human identities , 46% confirmed, 26% suspected.

Questions worth separating out

Q: How should teams implement continuous control validation in data governance?

A: Start by converting policies into executable controls with explicit conditions, scope, and owners.

Q: When does manual governance review create more risk than it reduces?

A: Manual review becomes risky when the asset population, policy set, or change rate is too large for periodic checks to detect failures before they matter.

Q: What breaks when governance controls cannot explain why an asset failed?

A: Teams lose the ability to triage quickly, assign ownership, and prove remediation.

Practitioner guidance

• Define controls as executable rules Express each policy as a testable condition with clear scope, pass or fail logic, and an owner who can act when the control breaks.
• Prioritise exception-based workflows Route teams only to failing assets and require each failure to carry a reason code, a responsible steward, and a tracked resolution state.
• Preserve evidence for every control run Keep a complete history of checks, failures, and remediation actions so audit teams can verify what was tested and when it was resolved.

What's in the full announcement

Collibra's full post covers the operational detail this post intentionally leaves for the source:

• The exact no-code query builder flow for defining validation logic across metadata fields.
• The scheduled control execution model, including how pass and fail states are displayed over time.
• The alerting workflow that routes failures into email, Slack, or Microsoft Teams.
• The product team's examples for BCBS 239, data freshness, and AI policy validation.

👉 Read Collibra's post on automating trust with Control Tower controls →

Control Tower controls: what they mean for data governance teams?

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