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24/06/2026 9:05 pm
TL;DR: Experiment, training, and deployment metadata can be pulled into a governed registry through new Azure AI Foundry and MLflow integrations, improving lineage, ownership, and compliance across decentralized model workflows, according to Collibra. The real issue is not integration coverage but whether AI governance can keep pace with fragmented development platforms before models become opaque operational assets.
NHIMG editorial — based on content published by Collibra: Integrating Collibra with Azure AI Foundry and MLflow, new integrations expand scope of model governance
By the numbers:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job.
- 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 teams govern models built across multiple AI platforms?
A: Teams should require one governed record per production model, even when development happens in several platforms.
Q: Why does model lineage matter for AI governance?
A: Model lineage matters because it shows how a model was produced, what data influenced it, and which version reached production.
Q: What do security and compliance teams get wrong about model registries?
A: They often treat registries as inventory tools rather than control surfaces.
Practitioner guidance
- Centralise production-model evidence Require every production model to have a single governance record that links experiment history, deployment state, owner, and approved business use case.
- Block unauditable model deployments Refuse promotion when a model cannot be traced back to source experiments, training runs, and the dataset or data product used for release.
- Attach policy to lifecycle state Bind policy assignment, owner review, and retirement criteria to the model lifecycle so governance survives platform changes and cross-tool movement.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- A walkthrough of how Azure AI Foundry metadata is extracted and mapped into Collibra's governance model.
- The specific experiment, model, and deployment fields ingested from MLflow tracking and registry objects.
- Examples of how model ownership, lineage, and policy assignments are represented after ingestion.
- Use cases for data scientists, MLOps engineers, and risk teams that want implementation detail rather than analysis.
👉 Read Collibra’s analysis of Azure AI Foundry and MLflow governance integrations →
Azure AI Foundry and MLflow governance: what changes for teams?
Explore further
25/06/2026 6:05 am
Model governance breaks when development platforms outrun the registry. Collibra’s integrations address a structural problem in AI governance: model evidence is scattered across experiment tools, registries, and deployment systems before anyone can classify it. That fragmentation turns lineage into a reconstruction exercise instead of a living control. Practitioners should treat cross-platform metadata harmonisation as the minimum condition for governance, not an enhancement.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to The 2026 Infrastructure Identity Survey.
- Another finding from The 2026 Infrastructure Identity Survey shows that 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems.
A question worth separating out:
Q: How do teams know if AI model governance is actually working?
A: Governance is working when every deployed model can be traced to an owner, a policy set, a release decision, and the data or workflow that shaped it. If teams still need manual reconstruction across platforms to answer those questions, governance is incomplete and accountability remains fragile.
👉 Read our full editorial: Azure AI Foundry and MLflow integrations widen model governance scope
