Topic starter
23/06/2026 7:00 pm
TL;DR: Many organisations cannot reliably say how many AI models are running in production, which Collibra frames as a governance failure that also creates regulatory exposure. The core issue is that AI governance policies without production-level model controls leave blind spots in ownership, monitoring, lineage and retirement.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “AI model governance: How to track, audit, and control AI models in production”.
Key questions
Q: What breaks when AI models are deployed without verified production ownership?
A: Teams lose the ability to prove what is live, who is accountable and whether the model still operates under the conditions that approved it.
Q: Why does model drift create governance risk even when code has not changed?
A: Because the approved object is no longer the same in operational terms once performance falls below its threshold.
Q: How do teams know whether model governance is working?
A: They can prove it when every live model is mapped to an owner, a model card, lineage data, monitoring thresholds and a retirement decision trail.
Practitioner guidance
- Verify the production model estate Reconcile the registry, deployment paths and downstream consumers so every live model has a named owner and a current risk tier.
- Attach governance evidence to each model Require model cards to include purpose, data sources, thresholds, approval history and change logs before a model can stay live.
- Wire drift alerts into governance workflows Escalate threshold breaches to the model owner and risk reviewers, then document whether the response is retrain, restrict or retire.
Bottom line: The article shows that the central AI governance problem in production is loss of control over what models are live, who owns them and whether they still meet approved thresholds.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
25/06/2026 2:46 am
Production model governance fails when inventory and reality diverge: A registry that records model intent is not a substitute for knowing what is actually running in production. That gap is the governance failure the article exposes, because ownership, monitoring and retirement all depend on a verified live estate. Practitioners should treat production discovery as the foundation of model control.
A few things that frame the scale:
- 13% of organisations suffered breaches of their AI models, according to IBM's Cost of a Data Breach 2025 report.
A question worth separating out:
Q: What is the difference between a model registry and a model governance system?
A: A registry tracks technical artifacts such as versions and training runs. A governance system tracks accountability, risk, compliance and control status across the full lifecycle. The registry is necessary, but on its own it cannot prove who owns the model, whether it has drifted or whether it is still approved for use.
👉 Read our full editorial: AI model governance in production needs stronger control and audit