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24/06/2026 7:30 pm
TL;DR: Fragmented AI assets across Vertex AI, Azure, Databricks, and SageMaker make ownership, lifecycle status, and trust evidence hard to track, according to Collibra. The real issue is that AI oversight breaks when the system of record for use cases, models, and agents is scattered across platforms, which is why a unified registry is being positioned as governance infrastructure rather than just an inventory tool.
NHIMG editorial — based on content published by Collibra: Unified AI registry: Your central inventory for AI use cases, models and agents
Questions worth separating out
Q: How should organisations centralise AI use case and model inventories?
A: They should require one authoritative registry for every AI use case, model, and agent, with ownership, lifecycle stage, and governance context captured at registration.
Q: Why do fragmented AI inventories create governance risk?
A: Fragmented inventories create governance risk because no single team can reliably prove what exists, who owns it, or whether it is still active.
Q: How can security teams make AI trust scores useful?
A: They should connect each trust score to the evidence behind it, including documentation, lineage, lifecycle progress, risk classification, and compliance status.
Practitioner guidance
- Create a single AI asset inventory Register every use case, model, and agent in one governed system of record, and require ownership, lifecycle stage, and business purpose at creation time.
- Tie trust scoring to evidence records Expose the documentation, lineage, compliance, and lifecycle inputs behind each trust score so reviewers can validate the result instead of accepting a summary number.
- Embed registry updates into delivery workflows Make asset registration and metadata refresh part of model release, change management, and retirement steps so the registry stays current as projects move.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- The exact registry workflow for registering AI use cases, models, and agents across teams
- How Collibra structures lifecycle status and trust metrics inside the unified registry
- The interface-level details for navigating from the inventory into individual AI asset records
- The product positioning for governance onboarding and AI portfolio oversight
👉 Read Collibra's post on the unified AI registry for AI governance →
Unified AI registry: what it means for AI governance teams?
Explore further
25/06/2026 4:49 am
AI governance fails when the system of record is platform-local. A registry embedded only in individual AI platforms creates fragmented accountability, because ownership, lifecycle stage, and trust evidence no longer line up across the enterprise. That fragmentation is not a reporting inconvenience. It is the point where governance loses the ability to compare, challenge, and certify AI assets consistently, which means practitioners should treat central inventory as a control plane, not a convenience.
A few things that frame the scale:
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to The State of Secrets in AppSec.
- Organisations maintain an average of 6 distinct secrets manager instances, creating fragmentation that undermines centralised control, according to GitGuardian & CyberArk.
A question worth separating out:
Q: What should teams do before moving AI systems into production?
A: They should confirm that each AI system has been registered, assigned an owner, given a lifecycle status, and attached to a review path for governance exceptions. Production should not be the first place an AI asset becomes visible. The registry should already exist before deployment begins.
👉 Read our full editorial: Unified AI registry exposes the governance gap in enterprise AI sprawl
