Neither model is sufficient on its own. Human access governance does not handle runtime autonomy well, while machine access governance assumes scope is stable and predictable. Agent access needs both delegation awareness and action-time enforcement, because the trust boundary sits between the human, the agent and the system.
Why Agent Access Does Not Fit a Single Identity Model
Agent access is not just a renamed version of human access or machine access. A human user brings intent and accountability, while a machine account is usually expected to have fixed scope and predictable behaviour. An agent sits between those models, because it can act with delegated authority, make runtime decisions, and change which actions it attempts based on context.
The practical consequence is that access policy cannot stop at initial login or static entitlement review. It has to answer two separate questions: who empowered the agent, and what can the agent do at the moment it acts. That makes agent access a distinct governance problem rather than a simple reuse of existing account patterns.
One useful way to think about this is to treat the agent as an identity-bearing actor with delegated authority, not as a human session and not as a conventional daemon account. The account or token may be machine-like, but the trust decision is shaped by delegation, ownership, and the scope of action the agent can exercise on behalf of someone else.
Where Human Access Controls Break Down
Human access governance is designed around people making bounded, observable decisions with clear login sessions, approval checkpoints, and reviewable intent. That works reasonably well when a person is the one clicking, approving, or entering data, but it becomes incomplete when an agent can continue acting after the human has left the session or when the agent can choose among several tool paths.
In agent workflows, the question is often not whether the human was authorised to start the task, but whether each downstream action should be allowed independently. If an agent can call tools, access data, or submit transactions, the control point shifts from identity proof alone to per-action authorisation. That is where least privilege and just-in-time agent authorisation become more relevant than standard user review patterns.
This is also why human-oriented approval workflows can create a false sense of safety. A single approval at task start does not necessarily cover later tool use, cross-system side effects, or chained actions that the human did not foresee. The right question is whether the agent is being governed as a bounded delegate or as a proxy with open-ended reach.
Where Machine Access Controls Also Fall Short
Machine access models assume the caller is stable, narrowly defined, and performing a known function. That assumption breaks when the same agent can vary its behaviour, select different tools, or request broader access depending on context. A service account can be managed as a static credential, but an agent often needs dynamic controls around which task it is performing, which resource it is reaching, and whether the action is still aligned to the original intent.
For that reason, agent access needs more than long-lived credentials and more than coarse role assignment. It needs action-time enforcement, visible attribution, and a revocation path that can be triggered when behaviour drifts. NHIMG’s AI Agent Observability, Audit and Incident Response Guide is directly relevant here because once an agent can act autonomously, logging and kill-switch design become part of access control, not just after-the-fact monitoring.
In practice, the strongest designs combine delegation, policy checks, and observability. That is the point where agent access stops looking like ordinary machine access and starts looking like a governed runtime authority problem.
Risk and Threat Considerations
Agent access creates a larger attack surface than either human access or machine access alone because it can amplify both stolen credentials and overly broad delegation. If an attacker hijacks an agent’s token, abuses its tool permissions, or manipulates its inputs, the resulting actions can look legitimate while still producing real business impact.
Failure mechanism: static identity controls, such as a one-time login decision or a long-lived service credential, can miss the fact that the agent’s authority changes at runtime and may be exercised across multiple systems without fresh human review.
Impact: compromise can lead to overreach, unauthorised tool use, data exposure, or lateral movement with the appearance of normal delegated activity, which makes detection and rollback harder than with a conventional user session.
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 SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-04 — Insecure Authentication | Agent access depends on how delegated identities are authenticated and used at runtime. |
| NHI-05 — Overprivileged NHI | Agents commonly accumulate excess permissions when treated like static machine accounts. | |
| NHI-07 — Long-Lived Secrets | Agent tokens and credentials often persist longer than the task they enable. | |
| Recommendation — Use stronger runtime authentication and reduce reliance on reusable agent secrets. Apply least privilege and remove permissions the agent does not need for each task. Rotate or replace long-lived agent secrets with short-lived credentials wherever possible. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent access is fundamentally about delegated identity and action authority. |
| ASI02 — Tool Misuse | Agent access determines which tools an agent can invoke and under what policy. | |
| Recommendation — Enforce per-action authorisation and constrain delegated privileges to the task. Restrict tool invocation to approved actions with explicit policy checks. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Service Identification and Authentication | Agent identities often function like services or workloads that must authenticate securely. |
| AC-6 — Least Privilege | Agent permissions should be bounded more tightly than broad human or machine roles. | |
| AU-2 — Event Logging | Agent actions need auditability because authority is delegated and dynamic. | |
| Recommendation — Authenticate agent-to-system interactions with service-appropriate credentials and controls. Limit each agent to the minimum access needed for the current action. Log agent actions with enough detail to attribute, investigate and revoke access. | ||
| NIST Zero Trust (SP 800-207) | N/A — Zero Trust Architecture | Agent access requires continuous verification and action-level trust decisions. |
| Recommendation — Verify the agent and the request continuously before granting access. | ||
Practitioner Guidance
What to verify: confirm that every agent has a named owner, a defined delegation boundary, and an explicit rule for which actions require per-request evaluation rather than session-level approval. If you cannot state where the agent’s authority begins and ends, the access model is still too human-centric or too machine-centric.
Decision rule: if the agent can change behaviour, choose tools, or operate across systems, treat it as a delegated runtime actor and enforce action-time policy decisions. If it only runs a fixed job with no autonomy, machine-style controls may be enough, but the moment it reasons, routes, or escalates, you need tighter governance.
Practitioner takeaway: the safest operating model is neither “treat agents like users” nor “treat them like daemons”, but to bind each agent’s delegated authority to observable, revocable, per-action controls.
Related resources from NHI Mgmt Group
- What is the difference between governing human access and governing AI agent access?
- When should organisations treat an AI agent as a privileged system?
- When should organisations treat a machine identity like privileged access?
- Should organisations treat agent access reviews the same as human access reviews?