Because they can combine reading, reasoning, and acting inside one workflow. That breaks the assumption that access is static enough to review later. IAM and PAM teams have to govern what the agent can do at runtime, including approvals, session limits, and the ability to stop unsafe action chains before they complete.
Why This Matters for Security Teams
AI agents change the access model because they do not behave like users with stable job functions. They can decide what to read, which tools to call, and when to act, all inside a single workflow. That makes traditional IAM review cycles and PAM session controls insufficient if they only validate identity at login. Current guidance suggests treating the agent as a runtime actor, not a static account, with policy checks tied to each action.
That shift is already showing up in the field. SailPoint’s AI Agents: The New Attack Surface report found that 80% of organisations say their AI agents have already performed actions beyond their intended scope, while only 52% can track and audit the data those agents access. That is not a narrow IAM issue. It is a control-plane problem spanning approval, credential lifetime, tool access, and revocation. OWASP’s OWASP Agentic AI Top 10 frames this risk clearly: autonomous systems can chain actions faster than manual review can intervene.
In practice, many security teams encounter agent overreach only after sensitive data has already been accessed or a downstream system has already been modified, rather than through intentional design of runtime guardrails.
How It Works in Practice
The practical answer is to move from static entitlement thinking to workload identity and context-aware authorisation. An agent should prove what it is with a cryptographic workload identity, then request only the access needed for the current task. That usually means short-lived credentials, just-in-time approvals, and policy evaluation at request time rather than at annual review. NIST’s AI Risk Management Framework supports this kind of runtime governance because it emphasises measurement, monitoring, and accountability for AI behaviour.
For IAM and PAM teams, the operational pattern is becoming clearer:
- Bind the agent to a workload identity, not a shared human-style account.
- Issue ephemeral secrets per task, then revoke them when the task ends.
- Require policy-as-code decisions for high-risk actions, including data export and privilege escalation.
- Log tool calls, approvals, and action chains so investigators can reconstruct what happened.
This is where frameworks such as CSA MAESTRO agentic AI threat modeling framework and NHIMG’s OWASP NHI Top 10 are useful, because both push teams to model action chains, not just login events. The real design goal is to let the agent do one bounded job safely, then lose access before it can wander into adjacent systems. These controls tend to break down when agents share broad tool tokens across multiple workflows because revocation, attribution, and blast-radius containment become indistinct.
Common Variations and Edge Cases
Tighter runtime controls often increase orchestration overhead, requiring organisations to balance safety against developer velocity and operational complexity. That tradeoff becomes sharper in multi-agent pipelines, where one agent hands off context to another and each hop can multiply privilege if the chain is not constrained. Best practice is evolving, but there is no universal standard for how much autonomy should be pre-approved versus re-authorised at runtime.
In lower-risk environments, teams may allow broader read-only access and reserve JIT controls for write or delete operations. In high-risk environments, especially where agents can trigger payments, deploy code, or query regulated data, the safer pattern is to combine real-time policy evaluation with human approval thresholds. The Ultimate Guide to NHIs and NHIMG’s AI LLM hijack breach analysis both reinforce the same lesson: once an agent can chain tools, static PAM session assumptions no longer describe the real risk. NIST AI Risk Management Framework guidance is especially relevant where organisations need formal accountability without pretending the agent behaves like a person.
These controls are strongest when the environment has clean service boundaries and a central policy engine. They are weaker in legacy estates with shared service accounts, unmanaged APIs, or opaque third-party plugins because the agent’s effective privilege becomes difficult to observe.
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 CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A01 | Agentic systems need runtime controls for autonomous tool use and action chaining. |
| CSA MAESTRO | MAESTRO addresses threat modeling for autonomous agent workflows and privileges. | |
| NIST AI RMF | AI RMF supports runtime accountability and monitoring for autonomous agent behaviour. |
Model each agent action as a policy check and block unsafe tool chains before execution.
Related resources from NHI Mgmt Group
- How should security teams govern AI agents that use OAuth access?
- How should security teams limit the risk from AI agents that have access to production systems?
- How should security teams govern AI agents that can access enterprise systems?
- Why do AI agents change the way IAM and governance teams think about access?
