Control Tower: OOTB controls to govern AI by exception

AI is only as trustworthy as the data and policies behind it.And yet, governance checks are still largely manual, inconsistent and impossible to scale as AI portfolios grow. In fact, Gartner predicts that by 2027, 60% of organizations will fail to realize the expected value of their AI use cases due to fragmented governance frameworks. The Collibra Control Tower solves the fragmentation challenge by offering prebuilt, automated controls that validate governance requirements continuously, so teams can manage AI by exception — instead of chasing every check by hand.

What's new: Control Tower OOTB Controls

The Collibra Control Tower introduces out-of-the-box (OOTB) controls for AI Command Center — prebuilt, configurable controls that automatically validate key governance requirements across your AI initiatives. Each managed control pairs a query that identifies failures with a run schedule and a custom failure notification, so policy validation runs continuously without manual intervention.

Rather than building governance logic from scratch, teams start from ready-made controls and adapt them to their policies. Controls are anchored in trusted metadata through the Collibra Control Tower connecting each check to its business, technical and compliance context. The result is policy enforcement that scales: AI use cases, models and the data that trains them are monitored automatically, and teams are alerted only when something falls out of policy — enabling true management by exception. Available now.

How Control Tower helps

As AI scales, the work of proving it's governed scales with it. Teams are expected to validate data quality, model status, ownership and policy compliance across a growing portfolio. But most of these checks are still manual, scattered across tools, and run too infrequently to catch issues in time. Without automated, reusable controls, governance becomes reactive and inconsistent, and high-risk gaps go unnoticed until they cause harm. Control Tower turns recurring governance requirements into prebuilt controls that run on a schedule, flag only the exceptions, and tie every result back to trusted metadata.

Problems Control Tower solves

• Manual, repetitive governance checks that don't scale with the AI portfolio
• Inconsistent policy enforcement across teams, tools and AI assets
• Late detection of issues like poor-quality training data or unapproved model versions
• Governance signals disconnected from business, technical and compliance context
• Alert fatigue — no way to focus attention only on what's actually out of policy

How Control Tower works

The Control Tower is a managed control object in Collibra and its configured in three steps:

1. Build a query to identify failures
2. Set a run schedule
3. Customize the failure notification

The screenshots show a control called "Data Quality Issues on Training Data," governed under a Data Governance Council and shown here in Candidate status with tabs for Summary, Failed Assets, Diagram, Pictures, Responsibilities, History and Attachments.

The control's logic is expressed as a query over trusted metadata — find an AI Base Model that has a version where status equals Accepted/Under review — that is trained by an asset containing a column contained in a data asset where the data quality Global Score is less than 70. In plain English, the control continuously looks for training data linked to approved AI models that have dropped below the mandatory quality threshold.

The control runs on a schedule — hourly in this example — and fires a custom message when a failure is detected: "A data asset currently linked to your AI model has fallen below the mandatory quality standards required for model integrity." Matching assets surface under Failed Assets for investigation and remediation.

Because controls are built on the Collibra Knowledge Graph, each one is anchored in real, governed relationships between models, versions, data assets, columns, owners and quality scores. This is what makes the controls trustworthy and reusable: they enforce policy using the same metadata, lineage and ownership already managed across the Collibra Platform, rather than a separate, siloed rules engine.

Why you should be excited

Control Tower empowers your team to shift from manual, reactive governance to automated, policy-driven oversight, allowing you to focus your attention where it matters most.

AI Governance Lead / Control Owners

• Deploy prebuilt controls instead of authoring governance logic from scratch
• Manage by exception — get notified only when an AI asset falls out of policy

Risk managers

• Continuously validate that approved models aren't trained on substandard or non-compliant data
• Prioritize remediation using Failed Assets and control health signals

Chief Data & AI Officers

• Scale policy-driven AI Command Center governance across the portfolio without scaling headcount
• Demonstrate automated, auditable enforcement of mandatory standards

Use cases

• Protecting model integrity from bad training data: the "Data Quality Issues on Training Data" control runs hourly and flags any data asset linked to an accepted AI model whose quality Global Score drops below 70, notifying owners before the issue degrades the model.
• Catching unapproved model versions: adapt the query to flag models in production whose version status is not Accepted, enforcing release policy automatically.

Key takeaways about OOTB Controls for AI governance

The Collibra Control Tower is where AI policy becomes automatic and enforceable. By packaging governance requirements as prebuilt controls that run on a schedule, surface only exceptions, and draw on the Control Tower, teams can move from manual, reactive checks to scalable, policy-driven oversight. The Tower helps govern AI from one connected source, turning policy into continuous, auditable action.

Three things to remember:

• Prebuilt OOTB controls enforce AI Command Center policies automatically — no logic to build from scratch
• Controls are anchored in the Control Tower for business, technical, and compliance context
• Management by exception reduces manual checks and scales policy-driven AI Command Center governance

Where to learn more about Control Tower

• Collibra AI Command Center product page
• Automating trust with Collibra Control Tower
• AI Governance dashboard: Introducing catalog metrics for AI visibility
  

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  Teresa Rojas

  Teresa Rojas

  Senior Director of Product Management, AI Governance

  Collibra

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  David Talaga

  David Talaga

  Product Marketing Director

  Collibra

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  Pankuj Chachra

  Pankuj Chachra

  Senior Director of Engineering

  Collibra