Topic starter
25/06/2026 1:18 am
TL;DR: AI model catalogs help teams find and reuse AI assets, while inventories create accountability for what runs, who owns it, and how risky it is, according to Collibra. Agents belong in both views, but unifying discovery and governance records is what keeps audits, oversight, and reuse from drifting apart.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “AI model catalog vs. AI model inventory: What's the difference (and where agents fit)”.
Key questions
Q: Why do organisations need both an AI model catalog and an AI model inventory?
A: They solve different problems.
Q: What breaks when AI asset discovery and inventory are missing from governance?
A: Without AI asset discovery, security teams lose visibility into models, notebooks, data lineage, agents, and toolsets.
Q: How should teams inventory AI agents that can take actions?
A: Teams should record agents as operational actors, not just as models with metadata.
Practitioner guidance
- Separate discovery from accountability Define the catalog as the builder-facing discovery layer and the inventory as the authoritative governance record.
- Register agents as acting assets Capture each agent’s owner, tool access, data touchpoints and lifecycle status in the inventory, because an agent can behave like more than one operational system.
- Unify records before creating tools Use one underlying registry that feeds both views so discovery metadata and governance metadata stay aligned as assets change over time.
Bottom line: AI model catalogs help teams find and reuse assets, but inventories are the control surface that determines ownership, risk and auditability.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
25/06/2026 10:38 am
Catalogs and inventories are complementary governance layers, not competing tools. The catalog solves search and reuse, while the inventory solves ownership, risk, lifecycle and auditability. When organisations collapse those functions into one view, they either overstate control or understate usability, and both outcomes create governance drift. The practitioner conclusion is simple: discovery and accountability need separate questions even if they share the same records.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: What is the difference between an AI model registry and a catalog or inventory?
A: A registry is the underlying record of AI assets. A catalog and an inventory are different views over that record, with the catalog optimised for discovery and reuse and the inventory optimised for accountability, audit and risk management. The registry is the shared source of truth, while the other two are purpose-built views.
👉 Read our full editorial: AI model catalog vs inventory: where agents fit in governance