A common mistake is treating AI agents and machine accounts like ordinary users, then granting access without enough constraint, lifecycle control, or review. That approach increases the chance of excessive privilege and weak accountability. Teams also fail when IT and OT governance are separated too sharply, because interconnections across environments often create the real exposure path.
Where access governance usually goes wrong
Organisations often start with the wrong mental model: they extend human access patterns to non-human actors and then rely on the same approval, recertification, and ownership habits that were designed for people. That breaks down quickly for AI agents and industrial systems because these actors are more automated, more distributed, and more likely to cross system boundaries without a clear business owner.
The practical failure is not “too much automation” by itself, but granting AI agents access without task-scoped policy, lifecycle limits, or action-level review. In industrial environments, the same mistake appears when access is treated as an IT issue only, even though the exposure often depends on how control systems, support tools, and upstream business applications are connected.
A second mistake is confusing authentication with governance. An agent or device can authenticate successfully and still be badly governed if it has broad standing privilege, weak separation of duties, or unclear offboarding when the workflow ends. For connected industrial systems, this is especially dangerous because long-lived trust relationships can quietly outlast the change that justified them.
Why AI agents and connected industrial systems change the access model
AI agents are not just another user class. They can act at machine speed, chain tools, and keep operating after the original requester has moved on, which means access decisions need to consider delegation, duration, and blast radius, not just login success. Agent identity, registration, and retirement matter because governance fails when the organisation cannot answer who owns the agent, what it may do, and how that authority is revoked.
Connected industrial systems change the picture in a different way. The main issue is not only direct compromise of a controller or workstation, but the trust chain that links IT systems, remote access paths, engineering tools, and operational networks. CISA’s industrial control systems guidance is useful here because it reflects the reality that segmentation, asset visibility, and disciplined remote access are part of governance, not just network hygiene.
That is why the same policy that works for employee access often fails here. AI agents may need just-in-time authorization for a narrow task, while industrial environments may need explicit separation between supervisory access, maintenance access, and production control. If those are flattened into one generic role model, the organisation creates invisible pathways that are hard to review and even harder to unwind.
What good access governance looks like across AI and OT
Good governance starts with defining the actor type and the decision boundary before granting anything. For AI agents, that means constraining what they may do per action, per tool, and per environment. For connected industrial systems, it means mapping which identities and connections are truly necessary across IT and OT, then limiting them to the smallest workable scope.
Use zero trust for AI agents as the operating model: verify the principal, remove standing privilege where possible, and make access decisions continuously rather than once at onboarding. For industrial connectivity, the equivalent discipline is to treat every bridge into operations as an exception that must be owned, bounded, and monitored.
Ownership is the other common weak point. If no single team owns the agent, the integration, and the downstream system, review will be superficial and revocation will be slow. The same is true for industrial access paths that sit between application, infrastructure, and operations teams: the real risk appears at the handoff points, not inside any one team’s catalogue.
Risk and Threat Considerations
The main risk is privilege accumulation across systems that were never designed to share a single trust model. AI agents can be abused to perform actions beyond their original purpose, while connected industrial systems can expose production environments through maintenance channels, remote tooling, or weakly controlled integrations.
Failure mechanism: An organisation grants broad, long-lived, or poorly reviewed access to an agent or connected system, then loses visibility into the exact actions that account can take across environments. That creates a practical path for misuse, lateral movement, or unintended operational change.
Impact: The result can be excessive privilege, weak accountability, and in industrial settings, operational disruption that is far harder to contain than ordinary IT misuse.
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 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | Excessive privilege is the core governance error in AI agents and machine accounts. |
| NHI-01 — Improper Offboarding | Agent and industrial access must end cleanly when the task or integration ends. | |
| Recommendation — Enforce least privilege and remove standing access from non-human identities. Define owner-approved offboarding and revoke access when the relationship expires. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI agents can be over-authorised or misused through broad delegated access. |
| Recommendation — Scope agent authority per action and require approval for sensitive operations. | ||
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Least privilege directly addresses broad access and excessive authority across systems. |
| IA-5 — Authenticator Management | Governance depends on controlling credential lifecycle for machines, agents, and integrations. | |
| Recommendation — Limit each account or agent to the minimum permissions needed for its task. Rotate, expire, and inventory credentials that enable automated access. | ||
Practitioner Guidance
What to prioritise: Start by inventorying the non-human actors that can actually execute actions, then separate “can authenticate” from “should be allowed to act.” If an agent or industrial account can reach multiple environments, treat that as a higher-risk condition until the access path is explicitly justified.
What to verify: Confirm that every agent, service account, or industrial integration has a named owner, a defined purpose, a review cadence, and a revocation path. If any of those are missing, the problem is governance, not just configuration.
Common mistake: Do not recertify these identities as if they were employee accounts. A human role review often misses standing machine privilege, cross-environment reach, and access that remains active after the workflow that created it has ended.
Practitioner takeaway: The best control is not more approvals, it is narrower authority with clearer lifecycle boundaries and better separation between business intent, automation, and operational execution.
Related resources from NHI Mgmt Group
- What do organisations get wrong when they separate AI security from SecOps and cloud governance?
- Why do AI agents increase risk when they are connected to HR systems with broad read access?
- What do organisations get wrong when they focus only on discovering AI agents?
- What do organisations get wrong when they onboard AI agents into IAM workflows?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 30, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
