AI agents need a third privilege model beyond users and service accounts

At a glance

What this is: This is an analysis of why AI agents need a distinct privilege model, because they behave differently from both human users and conventional service accounts.

Why it matters: It matters because IAM, PAM and NHI teams must govern delegated authority, tool use and approval paths for agents without creating a permanent admin tier.

By the numbers:

• Only 37% of surveyed organisations could revoke an AI agent’s credentials, according to Arcon.
• Only 30% had immutable audit logging for agent activity, according to Arcon.

👉 Read Arcon's analysis of why AI agents need a third endpoint privilege model

Context

Most endpoint privilege models still assume one of two principals: a human user or a conventional non-human identity. AI agents break that assumption because they can interpret a goal, select tools, process untrusted content and chain actions across systems, which means privilege must be governed by task, context and accountable sponsorship rather than by account type alone.

The practical problem is not whether an agent exists, but whether the organisation can define who initiated it, what it may do, which actions need approval and how its access ends. That shifts endpoint governance from static assignment to controlled delegation, and it makes AI agent authorisation a programme issue rather than a device setting.

Key questions

Q: What breaks when AI agents are governed like ordinary service principals?

A: The main failure is that ordinary service-principal governance assumes a stable workload with predictable lifecycle and entitlement patterns. AI agents can inherit access from a blueprint, act through user-shaped interfaces, and change their operational state at runtime, so the control model no longer matches the behaviour. Teams need a separate governance view for the agent runtime, not just for the underlying credential object.

Q: Why do AI agents increase endpoint privilege risk even when access looks limited?

A: Because limited access on paper can still expand through session inheritance, delegated tools and prompt-driven action selection. If the agent can use the user’s active session or invoke additional tools, the real privilege boundary is wider than the credential itself. Risk rises when governance measures the account but not the runtime path.

Q: How do teams decide whether an AI agent needs human approval?

A: Use the sensitivity of the action, not the cleverness of the model, as the decision point. If the agent can change records, move funds, send external messages, or access regulated data, human approval or an independent policy engine should remain in the path. The more irreversible the action, the less autonomy the agent should have.

Q: How can organisations keep AI agents accountable without creating a permanent admin role?

A: Keep accountability by tying each agent to a named sponsor, a specific approved task, a limited tool set and a complete audit trail. That preserves responsibility without inventing a standing administrator class. The goal is to govern delegated authority, not to centralise every decision in a permanent AI superuser.

Technical breakdown

Why AI agents do not fit conventional principal models

Traditional service accounts execute predetermined work. AI agents can receive an outcome, choose tools, sequence intermediate tasks and react to changing results, which makes the privilege question dynamic at runtime. That is why the relevant control problem is not only authentication, but delegated authority, tool scope and decision boundaries. The agent may still be a non-human identity, but it behaves like a contextual actor whose effective privilege changes with the task. If governance still treats it as a static application account, the programme will miss the point where risk actually emerges. See the Agentic AI Identity Guide.

Practical implication: Model agent privilege as task-scoped delegated authority, not as a permanent account class.

Why inherited user sessions expand the attack surface

When an agent borrows the logged-in user’s session, it can inherit browser cookies, SaaS sessions, mapped drives, cached credentials and local data stores that were never intended for machine use. That is not just excessive privilege. It is uncontrolled session inheritance, where the agent gains access simply because the user is already authenticated. The result is a weak boundary between human intent and machine execution, especially when the agent can invoke tools or modify content without a separate identity boundary. Read Zero Trust for AI Agents.

Practical implication: Separate agent identity from the human session wherever possible, and limit inherited permissions when you cannot.

Why approval gates must sit before consequential actions

AI agents can operate faster than human review cycles, so governance has to decide in advance which actions are reversible, which are not, and which require human approval. If an agent can install software, exfiltrate data or elevate privileges before a person can intervene, the control has already failed. The effective boundary is therefore not after execution, but before the action is authorised. That is the architectural difference between a normal endpoint process and an agent with delegated authority. See the AI Agent Authorisation Guide.

Practical implication: Place human approval and just-in-time elevation in front of high-impact actions, not after execution starts.

Threat narrative

Attacker objective: The objective is to turn delegated agent access into a path for unintended actions that extend beyond the user’s original intent.

1. Entry begins when an agent receives delegated access through a user session or a scoped credential that was not designed for machine-level decision making.
2. Escalation occurs when the agent interprets untrusted content or selects a broader tool path than the operator anticipated, expanding its effective privilege in-session.
3. Impact follows when the agent executes consequential actions such as data movement, software installation or privileged modification before human review can contain the chain.

Breaches seen in the wild

• CoPhish OAuth phishing via Copilot Studio: Datadog showed Copilot Studio agents on a Microsoft domain can front OAuth consent phishing and forward stolen tokens; no victims reported.
• Trivy supply chain attack 2026: A PAT stolen via a Trivy workflow and a botched rotation let TeamPCP poison Trivy releases and Action tags to steal CI/CD secrets.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI agents require a third privilege category: endpoint governance cannot treat them as either human users or conventional service accounts. Their runtime behaviour is contextual, not fixed, so the programme needs an operational category that follows task scope, tool scope and human sponsorship. The practical conclusion is that privilege models built on only two principal types are already incomplete.

Inherited trust is the real failure mode: when an agent borrows a human session, the organisation silently converts the user’s entire authenticated environment into machine permission. That is not a narrow exception, it is a governance collapse of the boundary between user intent and tool execution. The implication is that session inheritance must be treated as a control failure, not a convenience feature.

Machine agency changes the meaning of least privilege: least privilege for an agent is not a static assignment problem because the agent can select tools, chain actions and change its own next step from one prompt to the next. A fixed entitlements review cannot capture that behaviour. The implication is that the review unit shifts from the account to the task.

Complete attribution becomes part of privilege governance: if an agent can act on behalf of a person, the identity model must preserve the chain from initiating human to approved task to tool invocation and final action. Without that chain, accountability fragments even if the access policy itself looks sound. The practitioner takeaway is that attribution is no longer just logging; it is a governance control.

Scoped endpoint authority is now an identity problem, not just a device problem: endpoint privilege management, PAM and IGA have to work as one control plane when the actor is an agent. The important question is whether the organisation can issue, constrain and revoke authority fast enough to match machine-speed execution. The implication is that endpoint identity architecture must be redesigned around revocable delegation.

From our research library:

• Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
• Read next: Zero Trust for AI Agents

What this signals

Governance programmes built around static role assignment are already behind the problem. AI agents need controls that operate at issuance time, because the real risk is not just excessive privilege but the speed at which that privilege can be selected, chained and spent.

Ephemeral delegation gap: access review cycles assume the privilege survives long enough to be observed and recertified. AI agents can acquire, use and release authority inside a single task, so the control point shifts from periodic review to pre-execution authorisation and post-action attribution. according to the 2026 Infrastructure Identity Survey

The programme implication is simple: endpoint identity, PAM and approval workflows need to converge around the task, not the account. If the organisation cannot prove who sponsored the agent, which tool it used and when authority was removed, the control model is still permissive rather than governed.

For practitioners

• Define a third principal type for agents Classify AI agents separately from humans and service accounts in identity, PAM and endpoint governance so their access, approval and revocation rules are explicit.
• Replace standing privilege with task-scoped elevation Issue temporary authority only for the exact task, tool and duration the agent needs, and remove access automatically when the task ends.
• Block session inheritance where possible Prevent agents from inheriting the user’s browser cookies, SaaS sessions, mapped drives and cached credentials unless a compensating control is in place.
• Require approval for irreversible actions Route actions such as installation, privilege changes and data export through explicit human approval before the agent can execute them.
• Build attribution into the audit chain Log the initiating human, approved task, invoked tool, credential used and resulting action so the full delegation path can be reconstructed.

Key takeaways

• AI agents do not fit neatly into human-only or service-account-only privilege models, because they can make runtime choices that alter the real scope of access.
• Survey evidence in the article shows the governance gap is already operational, with only 37% able to revoke agent credentials and only 30% able to retain immutable audit logs.
• The limiting control is not another standing admin role. It is task-scoped delegation, human approval for consequential actions and a provable attribution chain.

Related NHI Mgmt Group resources: Agentic AI Identity Guide · AI Agent Authorisation Guide · Zero Trust for AI Agents · AI Agent Observability, Audit and Incident Response Guide

Key terms

• Agent Identity: An agent identity is the set of attributes, credentials and permissions assigned to an autonomous software entity. It is treated as a non-human identity because it can authenticate, act on systems and accumulate access over time, which creates governance, audit and lifecycle obligations similar to other production identities.
• Task-Scoped Access: Task-scoped access is permission granted for one defined purpose and removed once the task is complete or the session expires. For non-human identities, it reduces standing privilege and limits how long an attacker can exploit a stolen credential.
• Session Inheritance: Session inheritance is the transfer of a user’s active browser state, including cookies, tokens, and authenticated context, to an AI agent. In governance terms, it expands access beyond a single application and turns one login into a cross-application delegation boundary that must be controlled and audited.
• Complete Attribution: The ability to reconstruct who initiated an action, which task was approved, which credential was used and what the agent actually did. For agentic systems, attribution is a control requirement, not just a logging preference, because accountability breaks if the delegation chain is incomplete.

What's in the full article

Arcon's full article covers the operational detail this post intentionally leaves for the source:

• The endpoint privilege model mapping for human users, service accounts and AI agents side by side.
• The control questions for deciding which agent actions require human approval before execution.
• The preview Windows agentic-security pattern, including separate standard accounts and contained workspaces.
• The article's detailed framework-to-agent mapping across DORA, NIS2, PCI DSS and NIST CSF.

👉 Arcon's full article covers the principal model, control mappings and endpoint governance questions in more detail.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM or endpoint privilege programme, it is worth exploring.