They assume AI is only an application layer issue, when it often depends on the same credentials and permissions that govern other non-human identities. That separation hides the real control points, especially secrets, tokens, and service accounts. A unified identity view exposes where AI systems can overreach or leak data.
Why Security Teams Get This Separation Wrong
Separating AI risk from identity risk usually starts with an organisational blind spot: AI is treated like a model governance problem, while the real exposure sits in the same secrets, service accounts, and permission paths that govern every other Non-Human Identity. That split obscures who or what is actually authorised to act, which is why incidents often surface as data leakage, overbroad access, or unexpected tool use rather than a neat “AI issue.” The broader pattern is visible in NHIMG research on the Ultimate Guide to NHIs and the 52 NHI Breaches Analysis, where identity sprawl and weak control points repeatedly show up as root causes.
Industry guidance increasingly points the same way. The NIST Cybersecurity Framework 2.0 treats identity and access as a core control domain, not a side concern, and that matters because AI systems inherit whatever identity posture already exists. In practice, many security teams encounter AI overreach only after a token is reused, a connector is abused, or a service account is found with far more reach than anyone intended.
How Identity and AI Risk Interlock in Practice
AI risk becomes identity risk the moment an agent, workflow, or model integration is allowed to authenticate, retrieve secrets, or call downstream tools. At that point, the relevant question is not only “What can the AI output?” but “What can the underlying identity do, on whose behalf, and for how long?” That is why current guidance suggests treating AI workloads as first-class NHIs rather than as a separate governance category. The operational controls are familiar, but they must be applied to autonomous software behaviour.
Useful practice usually includes:
- mapping every model, agent, and connector to a distinct workload identity;
- issuing short-lived credentials per task instead of static long-lived secrets;
- scoping access by intent, context, and runtime policy rather than by static role alone;
- monitoring secret usage, token exchange, and tool invocation as identity events;
- revoking access automatically when a task completes or trust conditions change.
That approach aligns with the NIST AI Risk Management Framework, which emphasises governance and risk mapping, but it becomes far more practical when paired with NHIMG guidance on Key Challenges and Risks. Entro Security’s research on LLMjacking is a good reminder that attackers do not distinguish between “AI access” and “identity access” when a compromised credential opens the path. These controls tend to break down when AI is wired into legacy service accounts with broad standing privileges because the agent inherits human-era trust assumptions that were never designed for machine-speed chaining.
Where the Boundary Breaks Down Operationally
Tighter identity controls often increase operational overhead, requiring organisations to balance automation speed against governance depth. That tradeoff is real, especially when teams are trying to keep agent workflows usable while also reducing standing privilege and secret sprawl. Best practice is evolving, and there is no universal standard for this yet, but the direction is clear: the more autonomous the workload, the less defensible static IAM becomes.
The hardest edge case is a mixed environment where one AI service uses modern workload identity while another still relies on shared API keys, manually rotated tokens, or inherited cloud roles. Those hybrids create false confidence because a strong policy on one side does not compensate for a weak identity path on the other. NHIMG’s Top 10 NHI Issues and the DeepSeek breach both reflect the same lesson: once secrets and permissions are fragmented, AI risk and identity risk stop being separable in any meaningful operational sense.
Security teams should therefore review AI systems through the identity stack first, then ask what model-specific guardrails are still needed. That sequencing prevents the common failure mode where model policy is mature on paper but the underlying identities remain overpowered, long-lived, and easy to abuse.
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-03 | Static secrets and overprivileged NHI access are the core issue here. |
| OWASP Agentic AI Top 10 | A-02 | AI agents inherit identity risk when tool access is not context-aware. |
| NIST AI RMF | AI RMF requires governance that maps AI behaviour to operational risk. |
Inventory AI-related NHIs and replace long-lived secrets with short-lived, task-scoped credentials.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on June 23, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
