If the agent receives credentials, requests tokens, or calls APIs without human approval at each action, it should be treated as an NHI for governance purposes. That brings lifecycle, scope, logging, and revocation into the identity programme. The decision is based on execution behaviour, not on whether the system is branded as AI.
Why This Matters for Security Teams
The practical decision is not whether a system is called AI, but whether it can act independently in production. Once an agent can request tokens, call APIs, chain tools, or persist across tasks without a human approving every action, it starts to behave like a workload identity and should be governed accordingly. That shift matters because static role assignments rarely reflect what autonomous software will actually do at runtime.
Security teams also need to account for the pace and scale of agent behaviour. Agents can move faster than manual approval models, reuse credentials across tool calls, and expand their own reach through integrations that were never meant for human users. Current guidance suggests treating execution authority as the governance boundary, not branding or product category. This is why NHI controls around lifecycle, scope, logging, and revocation become relevant as soon as autonomy appears. NHIMG research shows that 97% of NHIs carry excessive privileges, which is a useful warning sign when teams are trying to map emerging agent estates to identity controls in the Ultimate Guide to NHIs. The same concern is reflected in OWASP Top 10 for Agentic Applications 2026, which highlights the new attack surface created by autonomous tool use. In practice, many security teams encounter over-privilege only after an agent has already chained actions beyond the original intent.
How It Works in Practice
IAM teams usually decide by testing the agent’s operational behaviour against a few identity questions: does it authenticate as a workload, does it need credentials to complete tasks, can it initiate actions without human approval, and can its permissions be scoped and revoked independently? If the answer is yes, the safest working assumption is NHI governance. The identity primitive is the workload, not the model name. That means using workload identity patterns, short-lived tokens, and policy evaluation at request time rather than granting broad standing access.
In practice, this is where modern controls start to align with agentic systems. Runtime authorization should be based on intent and context, not only on a pre-defined role. That is one reason policy-as-code approaches are gaining attention, including OPA-style decisions and emerging model-context policy patterns. JIT provisioning is also important: credentials should be issued per task, carry the shortest reasonable TTL, and be automatically revoked when the task completes. For implementation guidance, security teams often look to the NIST AI Risk Management Framework for governance discipline and to the CSA MAESTRO agentic AI threat modeling framework for threat-centric design. NHIMG’s 2024 Non-Human Identity Security Report notes that only 19.6% of security professionals are strongly confident in managing non-human workload identities, which matches the operational reality that many teams are still improvising access models for agents. The same report also shows that 59.8% of organisations see value in dynamic ephemeral credentials, reinforcing why static secrets are a poor fit for autonomous systems.
- Treat each agent as a distinct workload identity with its own issuance and revocation path.
- Use ephemeral credentials and short token lifetimes instead of reusable long-term secrets.
- Evaluate permissions at runtime using context such as task, resource, and risk posture.
- Log agent actions separately so approvals, tool use, and downstream effects can be reconstructed.
These controls tend to break down when legacy applications require shared service accounts or when one agent multiplexes many tenants through the same execution path because the identity boundary becomes too coarse to enforce safely.
Common Variations and Edge Cases
Tighter control often increases operational overhead, so organisations have to balance auditability and containment against deployment speed and developer friction. That tradeoff is especially visible in early-stage agent programs where teams want rapid experimentation but still need defensible governance.
There is no universal standard for this yet. Some organisations treat only fully autonomous agents as NHI, while others include semi-autonomous assistants as soon as they can access APIs or secrets without per-action approval. Best practice is evolving, but the safest rule is to classify based on capability, not intent labels. If an assistant can read data, write records, or trigger workflows on its own, it is already operating like an identity-bearing workload. This is also where the distinction between dynamic and static credentials matters: long-lived API keys may be tolerated for non-production pilots, but they are a poor match for production agents that can expand scope through tool chaining. NHIMG’s Top 10 NHI Issues and the OWASP NHI Top 10 both reinforce that privilege creep, weak rotation, and poor visibility are common failure modes.
Edge cases include human-in-the-loop systems, shared orchestration platforms, and multi-agent pipelines. In those environments, one component may be NHI-governed while another remains under human IAM, so control boundaries must be explicit. The practical test is simple: if the system can execute, persist, or delegate without a human at each step, it should be managed as an NHI for identity, access, and revocation purposes.
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 and CSA MAESTRO 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 Agentic AI Top 10 | A2 | Agentic autonomy and tool use create the core risk boundary here. |
| CSA MAESTRO | T1 | MAESTRO addresses threat modeling for autonomous agent workflows and identities. |
| NIST AI RMF | AI RMF frames governance for autonomous AI behaviour and accountability. |
Classify autonomous agents by runtime capability and restrict tool access with request-time policy checks.
Related resources from NHI Mgmt Group
- How do IAM teams decide whether an AI use case needs new controls or better NHI hygiene?
- How can organisations decide whether an AI agent belongs in PAM, IAM, or NHI governance?
- How do IAM teams decide whether an AI agent needs new controls?
- How do IAM teams decide whether an AI agent needs runtime policy enforcement?
