How should teams authorize AI agents without using shared service accounts?

Why per-user authorization is safer than shared service accounts for AI agents

Per-user authorization means the agent does not operate under one reusable machine credential for every task. Instead, the access decision is made for the person who approved the action, so the system can enforce task-scoped permissions, preserve attribution, and avoid collapsing many users into one shared trust boundary. That is the core shift teams need when agents act on behalf of people.

This design also gives you a cleaner control point: the service can decide what the approved user may do, while the agent client merely carries out the workflow. That matters because shared credentials make it impossible to tell whose authority was actually used, and they tend to outlive the task, the user session, and sometimes the original business need.

Teams that are modernizing this pattern often pair the access model with an AI agent identity reference such as Agentic AI Identity Guide, because the practical question is not just how the agent logs in, but how its delegated authority is represented, bounded, and retired.

What changes operationally when the service establishes effective permissions

When the service, not the agent client, establishes effective permissions, every action can be evaluated against the approved user, the task context, and the target resource. That gives teams a basis for per-action authorization, just-in-time elevation, and consistent policy enforcement across different models of agent execution.

It also changes incident handling. If a task produces an unexpected write, deletion, or disclosure, you can trace it back to the human approver and the exact policy decision rather than to a generic shared account. That is materially better for audits, post-incident review, and exception handling, especially when an agent spans tools, APIs, and back-end systems.

The architectural pattern is strongest when the authorization service, not the client, is the source of truth. That is the same principle behind AI Agent Authorisation Guide, which emphasizes task-scoped access, per-action policy decisions, delegated authority, and human approval.

For teams that need a standards-based way to express delegated authority without sharing a static secret, RFC 7523: JWT Profile for OAuth 2.0 Client Authentication and Authorization Grants is a useful reference point because it supports signed assertions instead of shared client secrets.

How to design the control so accountability is real, not just theoretical

Accountability only holds if each action is bound to a user, a policy, and a narrow time window. If the agent can reuse a broad token for many requests, you have only disguised a shared account. Good designs separate authentication of the service from authorization of the task, then log the approved user, the policy decision, and the action outcome together.

That separation is what keeps least privilege intact. The user should inherit only the permissions needed for the approved action, and those permissions should expire when the task ends or the approval is withdrawn. If the use case requires repeated actions, the right answer is often refreshed approval or step-up authorization, not a longer-lived shared credential.

In practice, teams usually need a lifecycle view as much as an authorization view, which is why Human vs Non-Human Identity is helpful when defining where the human decision ends and the agent’s delegated access begins.

For organisations that want a broader reference on identity, ownership, and lifecycle controls around machine-facing access, Non-Human Identity (NHI) Guide provides the surrounding control model for service accounts, API keys, workload identities, and governance.

Risk and Threat Considerations

Shared service accounts turn an AI agent into a high-blast-radius access path. If that credential is stolen, copied, or reused elsewhere, the attacker gets the same standing access that every workflow uses, and investigators lose the ability to separate legitimate agent action from abuse.

Failure mechanism: A shared account concentrates privilege, hides the approving user, and creates a reusable token or secret that can be abused outside the original task. Once that credential escapes, the attacker can impersonate the workflow rather than a specific person, which complicates containment and makes overbroad access harder to detect.

Impact: A compromise can lead to unauthorized reads, writes, deletions, session abuse, and lateral movement across connected systems. It can also undermine auditability, because all actions appear to come from the same principal even when the real risk is a specific approval path or a stale delegated token.

Practitioner Guidance

What to verify: Confirm that the effective permission is derived from the approving user for each action, not from a reusable client credential. If you cannot show which user authorized which action, the design still behaves like a shared account.

Decision rule: If a task can change production state, access sensitive data, or trigger downstream side effects, require per-action authorization and short-lived delegated access; if it is read-only and low impact, keep the same control model but with narrower scopes and stronger logging.

Common mistake: Do not treat a service token stored in a vault as a sufficient substitute for per-user authorization. A protected shared secret is still a shared secret, and it still collapses accountability when multiple people or automations use it.

Practitioner takeaway: The best control is not “an agent with its own account”, it is “an agent whose authority is explicitly borrowed, narrowly scoped, and revocable for each approved task.”

Related resources from NHI Mgmt Group

• Guide: Service Account Security Guide
• How can organisations govern AI agents that use service accounts and tokens?
• How should security teams authorize background agents without relying on standing service accounts?
• What happens when AI agents use shared service accounts without lifecycle control?
• How should security teams manage permissions for AI agents?