How should industrial and critical infrastructure teams structure identity and access governance when people, machines, and AI agents all need controlled access?

Teams should treat identity and access governance as an operational control, not just an administrative task. Start by defining who or what is allowed to access which assets, under what conditions, and with what privilege boundaries. In OT and regulated environments, the goal is to reduce standing access, preserve traceability, and keep access decisions aligned with resilience, compliance, and operational continuity.

Why identity governance has to cover people, machines, and agents together

Industrial and critical infrastructure environments rarely fail cleanly along human-only lines. Operators, service accounts, devices, APIs, scripts, and AI agents all create access paths that can bypass each other if they are governed separately. The right model is one policy plane for identity, privilege, and accountability, with different control treatments for each actor type but consistent rules for approval, traceability, and scope.

The practical shift is to manage access as a system of actors, not a list of user accounts. People need role and exception handling, machines need tightly scoped non-interactive access, and AI agents need delegated authority that is narrower still. The common denominator is that every actor should have a clear owner, a defined purpose, and an expiry or review point tied to operational need.

That is why AI Agent Authorisation Guide is a useful pattern even outside pure AI deployments: it frames task-scoped access, just-in-time approval, and per-action decisions as the baseline for any autonomous actor. For industrial teams, the same thinking helps separate persistent operator access from temporary maintenance access and from machine-to-machine service permissions.

How to structure access boundaries without breaking operations

Start by classifying assets and actions by criticality, then assign the narrowest access model that supports the work. Read-only monitoring, control-plane operations, safety-related changes, and emergency intervention should not share the same privilege boundary. In OT and regulated environments, “can log in” is not enough, teams need to know whether the actor can observe, change, approve, or execute a safety-impacting action.

Use conditional access rules that reflect operational state. An engineer may need elevated access during a maintenance window, a script may need access only from a known host, and an AI agent may need access only when a specific workflow, data source, and approval chain are present. Standing privilege should be the exception, not the operating model.

For agent-style access, Zero Trust for AI Agents is a strong reference point because it emphasizes verification of the agent, the principal, and the request before any action is allowed. The same design principle translates well to industrial automation, where the control question is not just who connected, but whether this specific request is still safe in this context.

Where machine or workload identities are involved, Agentic AI Identity Guide is especially helpful because it treats identity lifecycle, delegation, and retirement as first-class controls. That lifecycle lens matters for service accounts and integrations too, because unowned or unexpired machine identities tend to become invisible long before they become dangerous.

What breaks first when governance is too loose

The first failure is usually not a dramatic breach, but privilege drift. Access accumulates through exceptions, emergency fixes, vendor support, scripts, and temporary agent permissions that never get removed. Over time, the environment ends up with actors that can do too much, for too long, from too many places.

The second failure is traceability. If a command, change, or data pull cannot be attributed to a specific actor and purpose, incident response slows down and compliance evidence gets weak. In critical infrastructure, that lack of attribution matters because restoration, safety investigation, and change rollback depend on knowing exactly which actor took which action.

The third failure is trust leakage across domains. A human credential reused by automation, an over-scoped token copied into a workflow, or an agent allowed to chain actions without review can turn a routine integration into an outage path. Top 10 NHI Issues is relevant here because it highlights the recurring control failures behind sprawl, excessive permissions, and unmanaged credentials.

Risk and Threat Considerations

Industrial and critical infrastructure identity sprawl creates both resilience risk and attack surface. If a human, machine, or AI agent can authenticate with broad standing privilege, an attacker who compromises any one of them can often reuse that trust to move laterally or trigger unsafe actions.

Failure mechanism: Excessive privilege, weak lifecycle control, and poor actor separation allow credentials or delegated rights to outlive their purpose, so compromise of one actor can cascade into production access, persistence, or operational disruption.

Impact: The likely result is not just unauthorized access, but degraded safety margins, slower incident containment, loss of action attribution, and higher recovery cost when systems must be rebuilt or access paths revalidated.

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, NIST CSF 2.0 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.

Framework	Control / Reference	Relevance
NIST SP 800-53 Rev 5	AC-2 — Account Management	Covers lifecycle ownership and review of all actor accounts in OT and critical infrastructure.
	AC-6 — Least Privilege	Directly supports narrow, task-scoped access boundaries for mixed actor types.
	IA-5 — Authenticator Management	Applies to credential lifecycle, rotation, and protection for human and non-human actors.
Recommendation — Centralize account ownership, review, and removal for every human, machine, and agent identity. Limit each actor to the minimum privileges needed for the specific operational task. Rotate, protect, and retire authenticators on a defined lifecycle for every identity.
NIST CSF 2.0	PR.AA-05 — Least Privilege	Matches the need to remove standing access and constrain operational authority.
	PR.AA-04 — Access Permissions Management	Supports access approval, review, and revocation across mixed identities.
Recommendation — Enforce least privilege and remove standing access wherever task-scoped access will do. Review and revoke access on a regular cadence across people, machines, and agents.
NIST Zero Trust (SP 800-207)	0 — Zero Trust Architecture	Fits the verify-each-request model needed when many actor types share critical access.
Recommendation — Verify the actor, context, and request before authorizing any action.
OWASP Non-Human Identity Top 10	NHI-05 — Overprivileged NHI	Directly addresses excessive privileges for machine and service identities in this access model.
	NHI-01 — Improper Offboarding	Covers the lifecycle failure of leaving machine and agent access active after need ends.
Recommendation — Audit non-human identities for excess privilege and remove unnecessary rights first. Retire identities and credentials promptly when the actor no longer has a valid purpose.
OWASP Agentic AI Top 10	ASI03 — Identity & Privilege Abuse	Applies when AI agents receive delegated authority that can exceed intended scope.
Recommendation — Constrain agent authority to the smallest safe scope and require approval for escalation.

Practitioner Guidance

What to prioritise: Build one inventory of all actor types, then tag each entry by owner, purpose, access scope, expiry, and break-glass status. If you cannot assign an owner, the identity is not governed enough to be trusted in a critical environment.

What to verify: Check whether every privileged action has a separate approval or policy path, and whether machine and agent credentials are rotated, time-bound, and observable. If a non-human actor can still act after the business need has ended, the governance model is already too loose.

What good looks like: People, machines, and agents can all operate, but each does so through narrow, attributable, and reviewable access. The strongest signal of maturity is that emergency access and autonomous access both leave an audit trail that operations and security can actually use.

Practitioner takeaway: The control objective is not to treat every actor the same, it is to make every actor governable on its own terms while keeping privilege, attribution, and review discipline consistent across the whole environment.