AI models are powering critical business decisions—but without governance, they risk becoming black boxes. According to Forrester’s State of AI survey, 40% of highly regulated enterprises are proactively merging data governance with AI governance frameworks.
That’s why we’re introducing Collibra AI Model Governance: a centralized capability to register, monitor and govern models across their lifecycle. Fully integrated with our AI Use Case Governance framework, it ensures every model is transparent, traceable and aligned to business outcomes—so AI can scale responsibly, not recklessly
What’s new: Collibra AI Model Governance
We’re proud to introduce Collibra AI Model Governance—a centralized capability that lets you register, monitor, and govern AI models across their entire lifecycle, regardless of where or how they’re built. With this feature, you can create a single system of record for all your AI assets, complete with lineage, metadata, policy checks, and collaboration features.
This new feature works hand-in-hand with Collibra’s AI Use Case Governance framework, acting as a key subset that ensures every model supporting a business-critical AI use case is trustworthy and well-documented. With this release, Collibra becomes the single place where your AI assets—both use cases and models—are governed together.
How Collibra AI Model Governance helps
Organizations are deploying more models than ever, but governance often ends at the dataset. Without full model traceability, AI use cases remain risky and opaque. AI Model Governance solves this gap by introducing essential capabilities like formal sign-offs, progress tracking, and native integrations—ensuring every model is governed from development through deployment.
Problems it solves:
- No centralized visibility or inventory of models
- Poor linkage between models and business AI use cases
- Inconsistent lineage and documentation
- Audit and regulatory risk
- Limited collaboration between technical and business teams
- Unclear linkage between models and the business use cases they support
- Models being reused without clear oversight, increasing the risk of errors
- Limited ability to identify, track, or resolve issues once models are in use
AI model registry overview and new AI model registering into Collibra AI model registry
How Collibra AI Model Governance works
Collibra AI Model Governance functions as a governed model registry that tracks model metadata, lineage, and usage across the lifecycle. It enables technical and business users to understand where each model fits within an AI use case—and how that model was built, validated, and deployed.
Here, our AI Model Lifecycle plays a crucial role in moving the needle in your AI journey. The lifecycle tracker will track which stakeholders have to fulfill what type of tasks at each critical moment in the AI lifecycle. Adding legal assessments in the ideation phases and bias checks directed to the specialized AI scientist during the development phase. Later on an ai security sign-off can be completed to fully approve the model that is now heading to production.
It integrates with a broad set of ML platforms to ensure organizations can govern models regardless of their technical stack. This includes major platforms such as Azure AI Foundry, MLflow, AWS SageMaker, and AWS Bedrock—supporting diverse model development and deployment environments.
With these integrations, metadata is automatically harvested and mapped to Collibra’s governance framework—connecting models to business context, policy workflows, and data lineage.
This model-level governance becomes a building block of broader AI Use Case Governance, where organizations can manage models, datasets, KPIs, responsible AI assessments, and risks together under one governance umbrella.
Why you should be excited about Collibra AI Model Governance
Collibra AI Model Governance can help all members of an organization from data scientists to risk and compliance:
- Data Scientists: Your models are automatically registered, documented and traced. Easily link model outputs to business use cases and KPIs
- MLOps teams: Consistent model governance across platforms (MLflow, Azure AI, SageMaker, etc.) Track retraining events and input/output drift over time
- Risk and Compliance: Audit trails for all models and their use in regulated AI applications . Map policies directly to model development and evaluation steps
- AI Leaders and Product Owners: See how each model supports AI use cases and whether it meets compliance or business requirements. Standardize AI delivery processes and reduce risk at scale
AI Model Governance helps these various personas:
- Register and govern all fraud detection models used in a financial AI use case
- Track healthcare recommendation models linked to regulatory approval processes
- Monitor model versions tied to product recommendation use cases across regions
New AI model registering into Collibra AI model registry
Complete bias check into Collibra AI Governance
Key takeaways
AI Model Governance is now part of Collibra’s broader strategy for AI Use Case Governance—delivering full lifecycle traceability and compliance at both the model and use case level. With integrations spanning major platforms, it covers any model on any infrastructure—giving organizations a single governed view of how AI is built and used.
Catch up on all our recent announcements by watching the full June launch webinar recording.
How to get started
To explore how model and use case governance come together:
- Read our blog: Extending traceability across the AI model lifecycle
- Visit the AI Governance solution page
- Talk to your CSM for a sandbox demo or integration walkthrough
