The normal IAM assumption that access is bounded by a human operator breaks down. An agent can read context, choose actions, and use connected tools in ways that expand impact far beyond a chat interaction. When that happens, the control problem shifts from authentication to runtime authorisation, action containment, and tool-level monitoring.
When the user is no longer the only actor in the control plane
Once an AI agent can take actions for a user inside an enterprise platform, the security question stops being “did the right person log in?” and becomes “what exactly can this delegated actor do, for how long, and under what policy?” That shift matters because the agent may combine context, tools, and authorization in ways a normal session never would.
In practice, the broken assumption is that access is a stable, human-bounded session. An agent can operate across multiple requests, switch tools, and preserve authority beyond the moment of approval, which means the platform must treat the agent as a separate actor with its own lifecycle, limits, and audit trail.
The most important consequence is that authorization can no longer be treated as a one-time gate at sign-in. It has to be evaluated at runtime, per action, with enough context to distinguish a harmless read from a high-impact write or destructive operation. NHIMG’s AI Agent Authorisation Guide is useful here because it frames least privilege around task scope, per-action decisions, and human approval where needed.
How agent delegation changes identity, privilege, and containment
AI agents create a delegation problem, not just an authentication problem. If the agent is allowed to act “on behalf of” a user, the platform has to distinguish the original user, the agent’s own authority, and the tool or service receiving the request. Without that separation, approvals, scopes, and downstream logs quickly become ambiguous.
This is why token exchange, impersonation, and delegated authority become central design concerns. The enterprise system needs a way to represent that the action was delegated, constrain which resources the agent may reach, and prevent the agent from inheriting the user’s full standing access by default. RFC 8693: OAuth 2.0 Token Exchange is the right external reference for that delegation pattern, because it shows how one authority can be exchanged for another without collapsing the distinction between them.
Containment also becomes a practical boundary, not a theoretical one. If an agent can read inboxes, edit records, call APIs, and trigger workflows, then a single prompt or tool call can expand into a much larger blast radius than the user intended. NHIMG’s Zero Trust for AI Agents and AI Agents vs Agentic AI both help with the practical distinction between a conversational assistant and an actor with execution authority.
What enterprise platforms need to monitor when agents can spend authority
The most visible failure mode is not just stolen credentials, but legitimate authority used too broadly, too fast, or in the wrong sequence. An agent can chain tool calls, follow hidden context, or mis-handle a prompt in a way that makes each individual step look normal while the overall behaviour becomes dangerous.
That means monitoring must move from “who authenticated” to “what the delegated actor actually did.” The useful signals are action-level auditability, tool invocation history, privilege scope at the time of use, and whether a human approval or policy decision was required but bypassed. NHIMG’s AI Agent Observability, Audit and Incident Response Guide is a strong fit because it focuses on attribution, logging, and kill-switch readiness when agent behaviour changes from expected to unsafe.
Enterprises should also watch for identity reuse and shared authority patterns that make one agent look like many users, or many users look like one agent. That is where runtime authorisation and monitoring matter most: if the same token, session, or delegated grant can be reused across tasks, the platform loses the ability to contain misuse or investigate impact cleanly. NHIMG’s Top 10 Agentic AI Identity Issues is especially relevant for the identity and privilege failure modes that emerge when agents operate at scale.
Risk and Threat Considerations
When agents can act on behalf of users, the main risk is privilege amplification through delegation, because the agent can turn one approved interaction into many downstream actions. That creates exposure to overbroad access, unintended writes, fraudulent approvals, token misuse, and hard-to-trace lateral movement across business workflows.
Failure mechanism: The platform treats the agent as if it were only a UI layer, so the agent inherits user authority too widely, retains it too long, or can chain tool calls without fresh policy checks.
Impact: A single compromised or misdirected agent session can cause data disclosure, destructive changes, policy bypass, and attribution failures that are much harder to unwind than a normal account compromise.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent-on-behalf-of access creates runtime identity and privilege abuse risk. |
| Recommendation — Enforce per-action authorization and limit delegated privileges for every agent request. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Identification and Authentication (Service and Non-Organizational Users) | Delegated agents and tool services need distinct authentication and trust boundaries. |
| AU-6 — Audit Record Review, Analysis, and Reporting | Action-level monitoring is needed to trace delegated agent behavior and abuse. | |
| Recommendation — Authenticate each non-human actor separately and bind actions to the correct identity. Review agent audit trails for unusual tool use, scope creep, and policy bypass. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust Architecture | Runtime verification and least privilege are central when agents act inside platforms. |
| Recommendation — Verify each request continuously and remove standing privilege from agent pathways. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | Agents acting for users can accumulate excessive authority across tools and workflows. |
| Recommendation — Reduce agent privileges to the minimum task scope and revoke unused access quickly. | ||
Practitioner Guidance
What to prioritise: Define the agent as a distinct actor in your access model, then decide which actions require per-call approval, which can be pre-authorised, and which must be blocked entirely. If the answer is “anything the user can do,” the scope is already too broad for safe enterprise use.
What to verify: Confirm that every high-impact tool call can be tied to a specific delegated grant, policy decision, and audit record. If you cannot reconstruct why the agent was allowed to act, you do not yet have defensible runtime authorisation.
Practitioner takeaway: The control objective is not to make agents powerless, but to make their power narrow, time-bound, attributable, and revocable before a delegated session turns into an uncontrolled operator.
Related resources from NHI Mgmt Group
- What are the main reasons AI agents struggle to achieve enterprise-scale deployment?
- How should security teams govern AI agents that can access enterprise systems?
- How should enterprises govern AI agents across multiple clouds and SaaS platforms?
- What breaks when AI agents can contact support on behalf of users?