Should organisations choose human approval or zero standing privilege for AI agents?

Human approval or zero standing privilege for AI agents?

Choose both, but for different jobs. Human approval is a control for exceptional or high-impact actions, while zero standing privilege sets the default access state so the agent has no permanent authority to abuse. For AI agents, the safer pattern is ephemeral access with per-action approval for exceptions, rather than standing access that depends on periodic review.

Why the two controls solve different problems

Human approval answers a decision question, should this action proceed now? Zero standing privilege answers an access question, should this agent have the authority available by default at all? Those are related but not interchangeable. An agent can still be dangerous if it holds long-lived access even when every sensitive action is “approved” later, because compromise, misuse, or overreach can happen between reviews.

For agents that can reach production systems, modify data, or use sensitive agent authorization should be scoped to the task and time window, not granted as a blanket capability. That is the core reason zero standing privilege matters: it limits blast radius before a request is ever made. Human approval is then reserved for actions that exceed the pre-approved envelope or that cross a higher-risk threshold.

In practice, the right mental model is not “approval versus privilege”, it is “privilege first, approval second”. If the agent can act without justification for long periods, approval becomes a weak compensating control. If the agent can only obtain access for a specific job and loses it automatically, approval becomes a governance layer rather than the only barrier to misuse.

What changes when the actor is an AI agent

AI agents often chain actions, call tools, and continue operating after the original user has left the session. That makes standing access especially risky because the agent may still possess usable authority when context has drifted, the task has changed, or the original human intent is no longer current. For that reason, zero trust for AI agents starts from verify-and-require, not trust-and-review.

Where the agent must authenticate to other systems, access should be issued as short-lived, task-bound credentials rather than durable entitlements. That is consistent with how the problem is described in the Agentic AI Identity Guide, which treats identity, delegation, registration, authentication and retirement as lifecycle controls, not one-time setup tasks. If you skip that lifecycle view, human approval can become a paper trail over a fundamentally overpowered runtime.

This is also why the strongest designs use approval for exceptions, not for routine dependence. Routine dependence on human approval creates bottlenecks and encourages broad standing access “just to keep work moving”. Exception-based approval keeps the common path narrow while still preserving escalation for destructive, external, or irreversible actions.

How to design the operating model

The most defensible pattern is default-deny access with ephemeral grants, plus human-in-the-loop approval for actions that are sensitive, cross-boundary, or difficult to roll back. That means the agent starts with no standing privilege, receives only the minimum access needed for the current task, and loses it automatically when the task ends or the approval expires.

A useful implementation pattern is described in the AI Agent Authorisation Guide: task-scoped access, per-action policy decisions, delegated authority and human approval together. That combination is stronger than either control alone because policy can pre-clear low-risk actions while approval gates the outliers. It also keeps the human role focused on judgment, not on routine babysitting.

Where the agent touches sensitive APIs, admin consoles, or production data, approval should be tied to a specific action and a specific time window. Where possible, reuse should be avoided, because standing tokens, shared credentials, or broad delegated sessions tend to outlive the original decision that justified them. If the control cannot show when access was granted, for what, and when it expired, it is not really ephemeral.

Practitioner Guidance

What to prioritise: Start by removing default authority from the agent, then decide which actions truly need human approval. If the only way a control works is by giving the agent durable access first, the design is backwards.

What to verify: Check that the agent can complete routine work with short-lived, task-bound access and that approval is only needed for exceptions. Validate expiry, revocation, and auditability, not just the approval workflow itself.

Common mistake: Treating human approval as a substitute for least privilege. Approval can slow misuse, but it does not eliminate the risk created by a standing credential or a permanently overpowered agent.

Practitioner takeaway: Use human approval to govern high-risk decisions, and zero standing privilege to remove unnecessary authority by default. For AI agents, the control failure to avoid is persistent access with occasional oversight, because that leaves too much blast radius between review points.

Related resources from NHI Mgmt Group

• Why do AI agents and other non-human identities create more risk when organisations rely on standing privilege?
• Why do zero standing privilege strategies matter more as organisations adopt AI agents and ephemeral infrastructure?
• Should organisations prioritise zero standing privilege for AI agents before wider IAM modernisation?
• When is it crucial to implement least-privilege access for AI agents?