Non-human identities often outnumber human users and operate at machine speed, so their risk profile is very different. They can hold persistent access, call APIs directly, and trigger actions without a human present. Separate governance reduces over-privilege, improves traceability, and prevents automated systems from becoming a hidden attack path.
Why This Matters for Security Teams
AI-enabled workflows do not just expand the number of identities in the estate; they change how access is used. Non-human identities can authenticate directly to APIs, chain calls across services, and execute actions faster than human review can keep up. That means the risk is not only over-privilege, but also silent automation that bypasses the controls designed for people. NIST’s NIST Cybersecurity Framework 2.0 is useful here, but it treats identity governance broadly rather than addressing autonomous workloads specifically.
NHIMG research shows how quickly secret exposure becomes operational risk. In the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research, exposed AWS credentials were observed being targeted by attackers in as little as 9 minutes. That speed matters because AI systems often rely on long-lived tokens, service keys, and API credentials that were never designed for autonomous decision-making. In practice, many security teams encounter abuse only after an AI workflow has already triggered unwanted actions, not through intentional review of each machine-to-machine hop.
How It Works in Practice
Controlling NHIs separately starts with recognising that a human identity and an agent identity are not interchangeable. A person logs in interactively, but an agent may authenticate continuously, act through tool use, and require access only for a narrow task window. Static, role-based IAM breaks down because the agent’s behaviour is not fixed in advance. Instead, current guidance suggests pairing workload identity with runtime authorisation, so each request is evaluated against context, task, and policy rather than just a preassigned role.
Practitioners typically combine four controls:
- Workload identity as the primary identity primitive, using cryptographic proof of what the agent is.
- Just-in-time credential issuance, with short-lived secrets that expire when the task completes.
- Runtime policy evaluation, so access is approved based on the current action and context.
- Separate logging and ownership, so agent actions are traceable without being blended into human user activity.
That model aligns with emerging agentic AI guidance from OWASP and with workload identity patterns such as SPIFFE, where the point is to authenticate the workload itself rather than trust a shared secret. It also fits the spirit of the Ultimate Guide to NHIs, which frames non-human access as a distinct governance problem, not a subset of employee IAM. Where agent behaviour is dynamic, policy-as-code and time-bound credentials are more reliable than standing entitlements. These controls tend to break down when a single agent is allowed to reach multiple tool ecosystems with shared credentials, because lateral movement becomes difficult to distinguish from normal orchestration.
Common Variations and Edge Cases
Tighter NHI control often increases operational overhead, requiring organisations to balance automation speed against the cost of policy maintenance and secret rotation. That tradeoff is especially visible in agentic workflows that span SaaS platforms, internal APIs, and data pipelines. There is no universal standard for this yet, but best practice is evolving toward separate service principals, scoped tokens, and explicit approval boundaries for high-impact actions.
One common edge case is a “human in the loop” agent that still uses a non-human identity for tool access. In that model, the person approves the intent, but the agent still needs its own identity, permissions, and audit trail. Another is shared service accounts embedded in orchestration layers, where many agents reuse the same credential. That approach simplifies deployment but destroys traceability and makes revocation risky. AI systems also create a separate concern: they can infer patterns from prior prompts or code, so secrets exposed once may be reproduced elsewhere, which is why The State of Secrets in AppSec is relevant to NHI governance as well. Organisations that treat agent access as “just another user” usually miss how quickly an autonomous workflow can escalate once its credentials are reused across tools.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Agentic systems need runtime controls, not static user-style permissions. |
| CSA MAESTRO | MAESTRO addresses governance for autonomous agents and their tool use. | |
| NIST AI RMF | GOVERN | AI RMF governance applies to accountability for autonomous workflow behaviour. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Short-lived secrets and rotation are central to controlling NHIs separately. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access management is directly relevant to NHI separation. |
Define separate identity, policy, and audit controls for each agent workload.
