Because an AI asset without an accountable owner or identity trail cannot be governed, recertified, or retired reliably. Ownership records also reveal which service accounts, API keys, and third-party integrations can alter, invoke, or exfiltrate data through the asset.
Why This Matters for Security Teams
AI inventories fail when they list models and applications but omit the identities that actually make them operational. An AI asset can be approved on paper and still remain ungoverned in practice if no one can trace which service account, API key, certificate, or human owner can change its behaviour, connect it to data, or retire it safely. That gap turns routine administration into an exposure problem.
This is not a theoretical edge case. NHI Management Group has documented how exposed credentials and weak identity visibility accelerate attacker access in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research, and the control logic is consistent with the NIST Cybersecurity Framework 2.0: if you cannot identify, protect, and govern the actors behind a system, you cannot manage its risk. In practice, many security teams encounter the ownership gap only after a leaked key, broken integration, or undocumented agent action has already occurred, rather than through intentional inventory hygiene.
How It Works in Practice
A useful AI inventory is closer to an identity register than a model catalog. It should record the AI system, the non-human identities it uses, the human or team owner, and the specific rights each identity holds. That includes runtime identities for agents, orchestration identities for pipelines, and secrets used by tools, plugins, and external APIs. The NHI Lifecycle Management Guide is useful here because inventory is only actionable when it connects creation, approval, rotation, review, and retirement.
Security teams usually get the most value by capturing four fields for every AI asset: what it is, who owns it, what identities it can use, and what systems it can reach. That makes recertification and offboarding possible without guessing. It also helps distinguish a passive model endpoint from an autonomous agent that can invoke tools, chain actions, or request data on its own. The Top 10 NHI Issues research is a useful reminder that unmanaged credentials and orphaned identities are not separate problems; they are usually the same control failure seen from different angles.
- Inventory the AI system, its owner, and every NHI it depends on.
- Record credential type, scope, rotation owner, and expiration behavior.
- Link each identity to the tools, datasets, and APIs it can access.
- Require a retirement path for both the asset and its identities.
For governance, this also supports intent-based review: the question is not just whether the model is approved, but whether the identities behind it are still necessary and correctly scoped. These controls tend to break down in federated environments with third-party orchestration because ownership, rotation, and revocation responsibilities become split across teams and vendors.
Common Variations and Edge Cases
Tighter identity inventory often increases administrative overhead, requiring organisations to balance governance depth against the cost of keeping records current. That tradeoff becomes sharper in fast-moving AI programs, where agents are created ad hoc, reused across workflows, or delegated to external platforms.
Best practice is evolving for these cases, but current guidance suggests treating shared or ephemeral identities as first-class inventory objects rather than exceptions. If a single agent can act under multiple credentials, the inventory should show each credential’s owner and purpose. If the system relies on temporary tokens, the inventory should still show where those tokens are issued, who can request them, and what happens on task completion.
This is especially important when AI systems sit inside product teams, research sandboxes, or vendor-managed environments. In those settings, the inventory may look complete while the actual control surface remains outside central visibility. NHI Management Group’s analysis in the Ultimate Guide to Non-Human Identities shows why identity sprawl and weak lifecycle ownership keep recurring. When ownership cannot be assigned to a person or team with authority to act, inventory data becomes descriptive rather than governable.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Inventorying NHIs and owners directly addresses identity sprawl and orphaned access. |
| OWASP Agentic AI Top 10 | AGENT-03 | Agent inventories must capture tool access and accountability for autonomous actions. |
| NIST AI RMF | GOVERN | AI governance depends on clear accountability and traceable identity records. |
Record every AI-related NHI with a named owner and retirement path before approving production use.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 11, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org