They often separate AI governance from IAM and lifecycle management, even though AI adoption depends on who can access tools, what data those tools can reach, and how access ends. A policy that ignores procurement, revocation, and exception management will miss the identities that create the risk.
Why This Matters for Security Teams
Most organisations get AI governance wrong by treating it as a policy exercise instead of an identity and access problem. AI tools, copilots, and agents are only as safe as the accounts, tokens, APIs, and data paths behind them. When access is approved once and then forgotten, the risk shifts from model behaviour to uncontrolled reach, especially when secrets, datasets, and external connectors are involved.
That is why NHI Management Group consistently frames AI adoption through lifecycle control, not just usage policy. The practical question is whether procurement, onboarding, exception handling, and revocation are governed with the same rigor as the tool itself. The gap is visible in incidents like the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research, where compromised identities become the path into AI services. NIST also makes clear in the NIST Cybersecurity Framework 2.0 that governance, risk, and access management are inseparable.
In practice, many security teams encounter AI misuse only after an exposed credential, overbroad connector, or untracked vendor integration has already expanded access beyond what the original policy intended.
How It Works in Practice
Effective AI governance starts by inventorying the identities involved, not just the applications. A single AI use case may rely on a human account, an API key, a service principal, a model provider token, and downstream data access. If each of those is governed in a different system, the organisation creates blind spots that policy documents cannot close. Current guidance suggests treating AI as part of IAM, secrets management, procurement, and asset lifecycle management at the same time.
That means defining approval paths for tool onboarding, mapping each AI system to an owner, and setting revocation triggers for contract end, staff change, or vendor compromise. It also means checking whether access is time-bound, scoped to specific tasks, and logged at the point of use. The Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs is useful here because AI access often behaves like other non-human identities: it is created for a purpose, should be monitored during use, and must be removed reliably.
- Require an owner for every AI tool, model endpoint, and connector.
- Bind procurement approval to data-classification and identity review.
- Issue short-lived access where possible, rather than long-lived shared secrets.
- Revoke credentials and integrations when the use case changes or ends.
- Review exceptions as operational risk, not administrative paperwork.
The best operational checkpoint is whether the organisation can answer, at any moment, who can invoke the AI system, what data it can reach, and how that access will be terminated. These controls tend to break down in fast-moving SaaS-heavy environments because access is often created outside central IAM and never revalidated.
Common Variations and Edge Cases
Tighter AI governance often increases workflow friction, requiring organisations to balance speed of adoption against control over data, secrets, and exceptions. That tradeoff becomes more pronounced when business teams buy AI tools directly, when developers embed model APIs into production code, or when an agent is allowed to act across multiple systems.
Best practice is evolving, but there is no universal standard for every AI governance scenario yet. Some teams focus on acceptable-use policy, while others prioritise technical enforcement through identity, secrets rotation, and endpoint monitoring. The stronger model is the one that can survive real operational change: mergers, vendor swaps, new connectors, and emergency access. The Top 10 NHI Issues and the Ultimate Guide to NHIs — Regulatory and Audit Perspectives both reinforce that auditability and revocation matter as much as initial approval.
Organisations also get caught by edge cases such as shared lab accounts, shadow AI plugins, and cross-border data flows where contractual controls lag behind technical access. In those environments, governance fails when it assumes the tool list is complete, because the real exposure usually sits in the hidden identities and forgotten integrations.
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 address the attack and risk surface, while NIST CSF 2.0 and 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 | AI governance fails when non-human identities are unmanaged. |
| NIST CSF 2.0 | GV.RM | AI governance must be tied to enterprise risk and access decisions. |
| NIST AI RMF | GOVERN | The question is fundamentally about accountable AI governance structures. |
Define ownership, oversight, and lifecycle controls for every AI system before deployment.
