AI agents and NHIs are different identity problems

At a glance

What this is: This is an independent analysis of why AI agents and non-human identities are not the same identity problem, and why that distinction changes security controls.

Why it matters: IAM, NHI, and platform teams need separate control models for autonomous behaviour and for machine credentials, or they will secure one layer while leaving the other exposed.

By the numbers:

• 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.
• 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.

👉 Read Silverfort's analysis of AI agents vs NHIs and identity governance

Context

AI agent identity risk and non-human identity governance are often discussed together, but they solve different control problems. An AI agent is a software system that makes runtime decisions and can initiate actions, while an NHI is a machine credential or workload identity used to authenticate a system or service. Conflating the two leads to controls that are technically busy but operationally misaligned.

For identity teams, the distinction matters because the control objective changes with the actor type. NHIs need credential hygiene, lifecycle discipline, and access containment. AI agents need action scoping, behavioural monitoring, and clear accountability for decisions made inside a session. The article argues that teams should stop forcing both into one model, which is a typical mistake in early-stage architecture discussions.

OWASP Agentic AI Top 10: agentic systems create risks that extend beyond classic secret management, especially when tools, memory, and action scope are combined. That makes the governance boundary between credentials and autonomous behaviour a practical design decision, not a terminology debate.

Key questions

Q: How should security teams govern AI agents that use existing machine credentials?

A: Treat the credential and the decision-maker as separate governance objects. The machine identity controls authentication and entitlement, while the agent requires runtime limits, approval boundaries, and behavioural logging. If teams only manage the secret, the agent can still take actions that exceed the business intent behind the access.

Q: Why do AI agents complicate least-privilege design?

A: Because least privilege assumes the actor's intent is known when access is granted. AI agents can reinterpret tasks, chain tools, and change behaviour as context shifts, so the privilege definition may no longer match the action that ultimately occurs. That is why action scope matters as much as permission scope.

Q: What breaks when teams treat AI agents like ordinary NHIs?

A: Teams often secure the credential but ignore the runtime behaviour. That leaves them with good secret hygiene and poor action control, which is enough to let an agent access data, call tools, or trigger workflows in ways the organisation never intended. The gap is governance, not just visibility.

Q: How do IAM teams decide whether an AI agent needs new controls?

A: They should ask whether the system can make independent decisions that change the sequence, timing, or selection of actions. If it can, standard NHI controls are incomplete and the programme needs behavioural oversight, explicit task scope, and offboarding rules for delegated authority.

Technical breakdown

How AI agent behaviour differs from machine identity

AI agents are not just automated scripts with a new label. They interpret context, choose actions, and can initiate workflows across APIs, databases, and other tools. That makes their security profile closer to runtime decision systems than to fixed machine credentials. A service account authenticates a workload and carries permissions. An AI agent can combine those permissions with dynamic reasoning, which changes how risk is expressed. The key technical distinction is that the agent's behaviour is not fully predetermined at provisioning time, even if its access path is built on top of NHIs.

Practical implication: model the agent's behaviour separately from the credentials it uses, because securing the secret alone does not constrain the action it can take.

Why NHI controls do not fully cover agentic AI

Traditional NHI controls assume predictable authentication events, known privilege sets, and stable lifecycle states. That works for service accounts, API keys, and certificates because their behaviour does not change at runtime. AI agents break that assumption by transforming access into action. They may use a valid NHI to call tools, but the security question becomes whether the action itself is appropriate, not just whether the credential is valid. This is why behavioural monitoring, approval boundaries, and action limits matter alongside identity controls.

Practical implication: add action-level guardrails and observability where existing NHI controls stop at authentication and authorization.

Lifecycle governance for AI agents and NHIs

Lifecycle governance applies to both categories, but not in the same way. NHIs are created, rotated, revoked, and offboarded as credentials and workloads change. AI agents also need lifecycle control, but the relevant question is whether the agent should still have a task, a tool, or delegated authority after the business need changes. For agents, lifecycle is tied to runtime scope and operational oversight as much as to provisioning. For NHIs, it remains tied to credential validity and access entitlements.

Practical implication: separate credential lifecycle from delegated task lifecycle so that offboarding an agent also removes the authority it exercised through linked machine identities.

Threat narrative

Attacker objective: The objective is to turn legitimate access into unintended autonomous action that exceeds both scope and accountability boundaries.

1. Entry occurs when an AI agent receives valid access through an underlying NHI such as an API token, service account, or delegated workflow credential.
2. Escalation occurs when the agent uses that access to choose actions dynamically, expanding from authenticated access to tool use, data retrieval, or workflow initiation beyond the original human expectation.
3. Impact occurs when the agent misuses scope, exposes data, or triggers unintended downstream actions that the organisation cannot explain from credential state alone.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI agent identity risk is a different class of governance problem than NHI security. NHIs are designed to be predictable credentials, but AI agents can decide what to do with those credentials at runtime. That means the same access token can produce very different risk outcomes depending on whether the actor is a static workload or a decision-making system. Practitioners should stop treating agentic behaviour as a minor extension of machine identity.

Least privilege is not enough when the actor can re-interpret the task. Least privilege was designed for known request patterns and bounded system behaviour. That assumption fails when an AI agent can choose tools, sequence actions, and continue operating as context changes. The implication is not simply tighter permissions, but a re-evaluation of how privilege is defined when intent is partially constructed during execution.

Action scope is becoming as important as credential scope. A valid NHI can authenticate a system, but it cannot by itself prove that the resulting action is appropriate, explainable, or reversible. This is where agentic governance diverges from classical NHI oversight. Practitioners need to recognise that access review alone will not capture runtime misuse when decisions happen inside the session.

Identity governance must split credential control from behavioural control. The article's core contribution is the reminder that one control plane does not fit both actors. NHIs need lifecycle discipline, secret hygiene, and entitlement control. AI agents need oversight over what they do with the access they inherit. The practical conclusion is that IAM and NHI teams should design separate guardrails before the two patterns blur in production.

New concept: runtime authority leakage. This is the gap created when an agent inherits machine access but then exercises more decision authority than the credential model intended. Runtime authority leakage is not a secret problem alone and not a pure application problem. It is the point where identity, policy, and action no longer line up, and that is where governance breaks first.

From our research:

• 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
• That visibility gap is why practitioners should also read OWASP Agentic AI Top 10 alongside the NHI governance model.

What this signals

Runtime authority leakage: the next governance failure will come from systems that retain valid machine access while exceeding their intended action scope. For practitioners, that means secret rotation and credential cleanup remain necessary, but they no longer describe the whole risk surface when an AI agent can decide what to do with valid access.

With 98% of organisations planning to deploy more AI agents in the next 12 months according to AI Agents: The New Attack Surface report, the control challenge is scaling faster than policy adoption. Identity teams should expect more agentic access paths to appear inside existing platforms before governance catches up.

The practical signal is that IAM, PAM, and NHI teams will increasingly need separate review logic for credentials, actions, and delegated tasks. That is the operational boundary between machine identity management and autonomous behaviour oversight.

For practitioners

• Separate credential governance from agent governance Map which access paths belong to service accounts, API keys, and workload identities, then identify where an AI agent can act through them. Treat the credential as one control point and the agent's decision boundary as another.
• Constrain agent actions, not just agent access Define explicit tool, data, and workflow limits for each agentic system, then verify that the limits hold at runtime. This should include approval gates for high-risk actions and logging that records the decision path, not only the login event.
• Review lifecycle offboarding for delegated AI authority When a system, workflow, or business objective changes, revoke the machine credentials and remove the delegated authority attached to the agent. Offboarding must include the permissions, prompts, and integrations that let the agent continue acting.
• Track behaviour anomalies separately from secret hygiene Build detections for unusual tool chaining, unexpected data access, and action sequences that exceed the documented use case. Secret rotation is still necessary, but it will not detect an agent using valid access in the wrong way.

Key takeaways

• AI agents and NHIs solve different identity problems, so they need different controls.
• The evidence shows a governance gap already exists, with most organisations lacking policy coverage for AI agents even as deployment expands.
• Practitioners should split credential management from behavioural oversight before agentic systems inherit more access than the programme can explain.

Key terms

• AI Agent: A software system that can decide and act at runtime using model output, tools, and context. In identity terms, the important point is not whether it is intelligent, but whether it can change action sequence and timing without a human approval gate.
• Non-Human Identity: A machine or workload identity used by software to authenticate and access resources. It is usually a credentialed entity such as a service account, token, or certificate, and its governance focuses on entitlement, lifecycle, rotation, and visibility rather than autonomous decision-making.
• Runtime Authority: The effective power an actor has after authentication, including what it can choose to do with granted access. For AI agents, runtime authority can exceed the narrow permission model if the system can select tools, sequence actions, or continue acting without fresh review.
• Behavioural Guardrail: A control that limits what an AI system is allowed to do after it has authenticated. It sits alongside identity controls and is used to constrain tool use, data access, workflow initiation, and escalation paths that a valid credential alone cannot safely govern.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by Silverfort: AI agents and non-human identities are different security problems. Read the original.