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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.
NHIMG editorial — based on content published by Collibra: AI model catalog vs. AI model inventory: What's the difference (and where agents fit)
Questions worth separating out
Q: How should organisations structure AI model catalogs and inventories?
A: Organisations should use the catalog as a discovery layer and the inventory as the authoritative accountability layer.
Q: Why do AI agents need to appear in both the catalog and the inventory?
A: Agents need both views because they are reusable assets and governed actors at the same time.
Q: When does a catalog become a governance risk?
A: A catalog becomes a governance risk when teams treat findability as proof of approval.
Practitioner guidance
- Define separate catalog and inventory use cases Use the catalog for discovery, reuse, and builder efficiency.
- Require a shared record model Build both views from one underlying registry so metadata entered once can support both reuse and oversight.
- Track agents as governed actors List each agent in the inventory with ownership, data access, tool permissions, and review status.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- The article's side-by-side field comparison for catalog and inventory records, useful when mapping AI asset metadata to governance workflows.
- The practical examples of where agents belong in both views, which helps teams decide how to structure AI governance records.
- The explanation of why a unified registry can support both discovery and oversight without duplicating records.
- The FAQ detail on whether a registry, catalog, and inventory are the same thing in practice.
👉 Read Collibra's analysis of AI model catalogs, inventories, and agents →
AI model catalog and inventory: what IAM teams need to govern?
Explore further
25/06/2026 10:38 am
Catalog and inventory confusion is a governance failure, not a taxonomy problem. The article is right that both views can share the same underlying records, but the real risk is treating findability as sufficient control. That breaks the accountability model that AI governance depends on, especially when ownership, risk tier, and access scope are separated from discovery. Practitioners should read this as a record-design issue, not a naming debate.
A few things that frame the scale:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, 46% confirmed and 26% suspected, according to The 2024 ESG Report: Managing Non-Human Identities.
- Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, with a quarter encountering multiple attacks.
A question worth separating out:
Q: What is the difference between a registry and an inventory for AI assets?
A: A registry is the underlying record set that stores AI asset data. An inventory is the governance view of that record set, focused on accountability, ownership, and control status. The registry can support discovery too, but the inventory is what makes oversight defensible.
👉 Read our full editorial: AI model catalog vs inventory: where agents fit in governance
