Model Inventory

Expanded Definition

Model inventory is the governed record of every model an organisation deploys, integrates, or relies on, including ownership, intended use, risk tier, lineage, versioning, approval status, and retirement state. In agentic AI and NHI security, the inventory is not just an asset list; it is the control plane that lets teams prove which models are authorised, where they operate, and which dependencies they introduce.

Definitions vary across vendors on whether the inventory should include only production models or also prototypes, fine-tuned variants, embedded foundation models, and third-party model endpoints. NIST’s NIST Cybersecurity Framework 2.0 reinforces the broader governance need: you cannot manage risk for systems you have not identified. For NHI Management Group, model inventory becomes especially important when models have tool access, can invoke APIs, or are wrapped in workflows that behave like autonomous agents.

The most common misapplication is treating model inventory as a procurement spreadsheet, which occurs when teams record vendor names but omit actual deployment context, owners, and lifecycle controls.

Examples and Use Cases

Implementing model inventory rigorously often introduces governance overhead, requiring organisations to weigh visibility and accountability against the effort of continuous updates as models change.

• A security team records each production model with owner, purpose, risk tier, and approved interfaces so that every model can be traced during an incident review.
• An AI platform team inventories fine-tuned versions separately from their base models because each variant has a different validation history and rollback path.
• A risk committee uses the inventory to flag models that access customer data or trigger actions through APIs, aligning the entry with controls described in the Ultimate Guide to NHIs.
• A procurement workflow requires that new models be added to the inventory before enablement, ensuring that licensing, data handling, and monitoring obligations are visible from day one.
• An incident responder checks the inventory to identify which model versions were active during a harmful output, then compares them against approved baselines and change records.

For AI governance terminology, the model inventory also complements broader identity and access practices described in the NIST Cybersecurity Framework 2.0, especially where models are granted execution authority rather than used as passive analytics assets.

Why It Matters in NHI Security

Model inventory matters because agentic AI systems can behave like privileged non-human actors once they are connected to tools, workflows, and secrets. Without an inventory, organisations cannot reliably monitor model sprawl, detect shadow deployments, enforce retirement, or prove which model had access to which resource at a given time. That gap becomes a governance failure, not just an operational nuisance.

NHI Mgmt Group reports that only 5.7% of organisations have full visibility into their service accounts, a reminder that visibility failures are common even before AI models are added to the estate. The same pattern emerges with models when teams cannot reconcile what is running, what is approved, and what still has active privileges. This is where model inventory intersects with zero-trust expectations and lifecycle control. The Ultimate Guide to NHIs shows how quickly unmanaged non-human identities expand attack surface, and model inventory is the comparable control for AI systems.

Organisations typically encounter model inventory as a critical issue only after a model is implicated in an incident, at which point the inventory becomes operationally unavoidable to reconstruct exposure, ownership, and containment.

Related resources from NHI Mgmt Group

• Asset Inventory
• Secrets Inventory
• Shared Responsibility Model
• Why is NHI discovery and inventory the primary goal of NHI security?