What should governance teams do with AI assets that exist outside the register?

Why This Matters for Security Teams

AI assets that sit outside the register are not just a bookkeeping problem. They are unmanaged identities, unmanaged secrets, and unmanaged behaviour paths. When a model, service, or agent is invisible to governance, it can be trained, deployed, or connected to tools without the controls that normally enforce ownership, retention, and review. That creates a gap between what engineering is running and what security believes exists. Current guidance from NIST Cybersecurity Framework 2.0 aligns with treating visibility as a prerequisite for control, not a post-incident cleanup task.

The issue becomes more serious when shadow AI carries credentials, API access, or retrieval links into sensitive systems. NHIMG research on lifecycle governance shows that unmanaged NHIs often persist because no one owns their retirement path, and that same pattern applies to AI assets that are never formally registered in the first place through the Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs. In practice, many security teams encounter model sprawl only after an access review, incident, or audit has already exposed the gap.

How It Works in Practice

The practical response is to treat unregistered AI assets as a governance exception until they are brought into inventory. That means engineering, platform, and security teams first reconcile what is actually deployed, then map each asset to an owner, purpose, environment, and risk tier. The point is not to block every unlisted workload immediately, but to stop it from continuing through the lifecycle without accountability.

Governance teams usually need three actions in parallel:

• Identify the asset through cloud inventories, CI/CD records, model registries, endpoint telemetry, and procurement records.
• Determine whether it is a model, wrapper, agent, embedded AI feature, or external AI service with data access.
• Assign a control owner who can approve use, enforce review, and retire the asset when it no longer meets policy.

That workflow should be tied to standard governance evidence, including data classification, secret handling, and change management. The risk is not limited to model quality. Unregistered assets frequently inherit secrets, tokens, and connectors from surrounding systems, which makes them harder to detect and easier to abuse. NHIMG’s Top 10 NHI Issues research reflects the wider pattern: invisible identities usually remain unmanaged until someone is forced to inventory them after the fact. For program design, that is why governance teams should align the AI register with the identity and secret inventories rather than treating them as separate exercises. These controls tend to break down when shadow AI is embedded in development pipelines or SaaS features because ownership is dispersed and no single team sees the full access path.

Common Variations and Edge Cases

Tighter inventory control often increases operational overhead, requiring organisations to balance speed of adoption against governance certainty. That tradeoff is most visible in environments where teams prototype AI tools rapidly or consume model capabilities through third-party platforms. In those cases, the register can lag behind reality, and the first priority is usually to classify exposure rather than freeze all use.

Current guidance suggests a few edge cases need special handling. An internal experiment may be low risk if it is isolated from production data, but the same experiment becomes a governance concern once it gains credentials, shared storage, or user-facing access. Embedded AI features inside commercial software can also be difficult to register because procurement, IT, and security may each assume another team owns them. Finally, ephemeral or short-lived models still need a record if they can process sensitive data or invoke tools.

The operational rule is simple: if the asset can influence data, decisions, or access, it belongs in the register even if it is temporary. Where organisations have weak discovery and fragmented ownership, that step often depends on audit pressure or incident response rather than proactive governance. For audit and lifecycle context, see Ultimate Guide to NHIs — Regulatory and Audit Perspectives and the 2024 ESG Report: Managing Non-Human Identities, which highlights how frequently organisations encounter compromised or suspected compromised NHIs once visibility gaps exist.

Related resources from NHI Mgmt Group

• What do security teams get wrong about AI governance inventories?
• When should teams tie AI governance to data governance?
• How should IAM teams handle systems that are outside their identity governance tools?
• What should security teams evaluate after a major AI governance acquisition?