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27/06/2026 1:28 pm
TL;DR: A current AI model and agent inventory is the baseline for governing models, use cases and autonomous agents because it ties each asset to ownership, risk and data access, according to Collibra. Without that record, shadow deployments, orphaned agents and stale spreadsheets turn AI oversight into guesswork.
NHIMG editorial — based on content published by Collibra: AI Model and Agent Inventory: How to Catalog Every AI System in Your Enterprise
Questions worth separating out
Q: How should security teams build a current inventory for AI models and agents?
A: Start by registering AI assets at the point of deployment, not in a quarterly spreadsheet review.
Q: Why do AI agents need separate governance from AI models?
A: Because agents do more than generate predictions.
Q: What breaks when an AI inventory is only updated manually?
A: Manual inventories drift almost immediately in fast-moving AI estates.
Practitioner guidance
- Register AI assets at deployment time Tie model, use case and agent registration to the deployment pipeline so every promoted asset creates an inventory record before production access is granted.
- Separate agents from models in the schema Track autonomous agents with their own fields for owner, lifecycle stage, framework dependencies, data access and tool reach instead of folding them into generic model records.
- Use discovery to expose shadow AI Compare the live estate against the inventory and flag any running AI system that lacks a corresponding record, especially where agents may have spawned other agents.
What's in the full article
Collibra's full post covers the operational detail this post intentionally leaves for the source:
- The full field-by-field inventory schema, including how Collibra structures asset ID, lifecycle stage and trust signal
- Practical examples of capture-at-source inside deployment workflows so registration happens from code rather than manual entry
- The article's distinction between AI model inventory and AI model catalog, including where reuse and governance diverge
- The FAQ detail on compliance, ownership and live updates for teams already building AI oversight processes
👉 Read Collibra's guidance on AI model and agent inventory design →
AI model and agent inventory: what IAM teams need to fix?
Explore further
27/06/2026 2:49 pm
AI inventory is the control point where AI governance becomes operational. A current inventory is not an administrative extra, it is the evidence layer that makes ownership, risk classification and access scope enforceable. Without it, AI oversight collapses into periodic guesswork and post-incident reconstruction. Practitioners should treat inventory quality as a prerequisite for every downstream governance decision.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to The 2026 Infrastructure Identity Survey.
- 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.
A question worth separating out:
Q: How do IAM and AI governance teams know if their inventory is working?
A: Look for low mismatch between approved state and live state, clear named ownership for every asset and a fast path from discovery to registration. If shadow systems routinely appear without a record, the inventory is functioning as documentation rather than governance, and the team still lacks control over the estate.
👉 Read our full editorial: AI model and agent inventory is now the first governance test
