Yes, because deployment automation, registries and pipeline services often hold the authority to move models and data between environments. Those machine identities can be as powerful as human admins, so they need ownership, scoped permissions and lifecycle review. Without that inventory, identity governance is incomplete.
Why inventorying non-human identities is part of ML operations governance
In ML operations, the practical question is not whether a machine identity exists, but whether it has a defined owner, purpose, scope and retirement path. Deployment services, CI/CD runners, workflow engines, registries and model promotion tools often move artifacts and data across environments, so their access needs the same governance discipline as any other privileged actor.
An inventory gives teams a usable boundary around who or what can publish models, pull training data, call APIs, sign artifacts or promote releases. Without that boundary, teams tend to manage access informally, which makes review, rotation, exception handling and offboarding unreliable. NHIMG’s Ultimate Guide to NHIs is a useful reference point for the broader governance pattern, and Human vs Non-Human Identity helps clarify where machine access differs from human access in ownership and lifecycle.
Inventorying also supports operational accuracy. If an ML platform cannot tell which service account, token, workload identity or integration user is responsible for each action, it becomes difficult to prove least privilege, investigate anomalous activity or answer basic questions about cross-environment trust. That is why inventory is not administrative overhead, it is the prerequisite for controlled automation.
What should be captured in an ML non-human identity inventory?
A useful inventory should capture more than a name and a credential. It should identify the identity type, owning team, business purpose, environment, systems it can reach, issuance method, renewal or rotation process, and the dependency chain that makes the access work. For ML operations, that usually includes pipeline runners, model registries, orchestration services, feature store integrations, data transfer jobs and any automation that can alter model or data state.
The inventory should also distinguish between identities that only read artifacts and identities that can change state, because the risk profile is very different. A read-only integration may need visibility and monitoring, while a build or deployment identity may need explicit approval, tighter scoping and shorter credential lifetime. NHI Lifecycle Management Guide is directly relevant here because lifecycle stages, not just discovery, determine whether the inventory stays trustworthy over time.
For ML operations, the inventory should be linked to the systems that issue and consume credentials. That helps teams see whether a token is embedded in a workflow, whether a certificate is tied to a deployment path, or whether a federated workload identity is replacing a static secret. NHI Authentication Guide and Machine Identity, PKI and Certificate Lifecycle Guide both support that control view.
Why inventory reduces ML security blind spots
ML environments are attractive to attackers because they often depend on many service-to-service relationships, each of which may carry credentials, privileges or trust assumptions. When identities are undocumented, defenders lose the ability to spot overprivileged automation, stale credentials, hidden third-party access and orphaned integrations. In practice, those gaps increase the blast radius of a single compromise.
The main failure mode is not that an ML platform lacks controls, but that teams cannot apply them consistently because they do not know what exists. If an identity is missing from inventory, it will usually also be missing from review, ownership assignment, rotation planning and deprovisioning. Service Account Security Guide and NHI Ownership and Accountability Guide are useful because they connect visibility to accountability, not just discovery.
In ML operations, the security consequence is especially pronounced when an identity can move data or artifacts between environments. That kind of access can expose training data, model weights, prompts, secrets or deployment controls. A complete inventory makes it possible to apply scoped permissions, review high-risk paths first and retire access that is no longer needed. NIST Cybersecurity Framework 2.0 supports the broader govern-identify-protect structure, and CIS Controls v8 reinforces the value of inventory, access control and account management as operational safeguards.
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 addresses the attack and risk surface, while NIST CSF 2.0 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | ID.AM-01 — Identities and permissions are managed | ML ops inventories depend on managing identities and permissions across automation. |
| PR.AA-05 — Least Privilege Access is Established and Managed | Scoped access for ML automation is a least-privilege problem. | |
| Recommendation — Inventory and govern every machine identity that can move models or data. Restrict ML automation to the minimum permissions needed for each workflow. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | ML non-human identities depend on controlling credential lifecycle and rotation. |
| AC-6 — Least Privilege | ML deployment and registry identities should only have the access they need. | |
| Recommendation — Track, rotate and retire authenticators used by ML automation on a defined schedule. Limit each ML service identity to the smallest access set required. | ||
| OWASP Non-Human Identity Top 10 | NHI-01 — Improper Offboarding | Undiscovered ML automation identities are often left active after use. |
| NHI-05 — Overprivileged NHI | ML pipeline and deployment identities often accumulate excessive rights. | |
| Recommendation — Remove ML automation identities as soon as the workflow or owner changes. Audit ML machine identities for permissions that exceed their actual task. | ||
Practitioner Guidance
What to prioritise: Start with identities that can promote, publish, sign or deploy model artifacts, because they create the largest blast radius if mis-scoped. If an identity can change production state, it belongs at the top of the inventory even if it is only used by automation.
What to verify: For each identity, verify owner, purpose, environment, credential type, rotation method and decommission trigger. If any of those fields are unknown, the inventory is not yet actionable for governance or incident response.
Common mistake: Treating pipeline tools as infrastructure only and leaving their identities out of access review. That shortcut usually hides the very access paths that move model and data assets across trust boundaries.
Practitioner takeaway: In ML operations, inventory is the control that turns machine access from an informal dependency into a governable asset, and without it every later decision about privilege, rotation or offboarding is weaker than it appears.