Zero trust and least privilege assume access can be defined, verified, and reviewed within stable identity boundaries. AI agents can change tool use and intent during execution, so the question is no longer only who may connect, but what the actor is trying to accomplish in real time. That shifts governance toward runtime inspection.
Why Traditional Zero Trust and Least Privilege Struggle with AI Agents
AI agents complicate zero trust and least privilege because they are not static users with fixed workflows. They are autonomous, goal-driven software entities that can choose tools, chain actions, and change direction mid-task. That means access decisions cannot rely only on a pre-assigned role or a one-time approval. Current guidance increasingly points to runtime inspection and context-aware authorisation, not just perimeter checks or RBAC.
This is why the conversation now overlaps with workload identity, JIT credentials, and ephemeral secrets. A human account can often be reviewed against a stable job function, but an agent may require access to a database, a ticketing system, and an API gateway in the same session, then never need that path again. The security challenge is not merely authentication, but continuous verification of what the agent is trying to do right now. NIST’s NIST AI Risk Management Framework and NIST SP 800-207 Zero Trust Architecture both support this shift toward decisioning that is dynamic, contextual, and continuously evaluated.
For agentic systems, the most useful NHIMG framing is the one used in OWASP NHI Top 10, where identity and privilege are treated as runtime problems rather than static entitlements. In practice, many security teams encounter over-privileged agents only after an autonomous action has already touched sensitive systems, rather than through intentional review.
How Runtime Authorisation and Ephemeral Credentials Change the Model
Practical control starts by treating the agent as a workload identity, not a long-lived account. That means issuing cryptographic identity to the agent instance or session, then binding permissions to intent, task scope, and expiry. In mature designs, an agent asks for access only when a tool call is needed, receives a short-lived token or secret, and loses it when the task ends. That is the difference between broad standing privilege and true Guide to SPIFFE and SPIRE-style workload identity, where the system proves what the agent is and what it is allowed to do at that moment.
Effective teams are also moving from pre-defined roles to policy evaluation at request time. Instead of saying “this agent can manage infrastructure,” policy engines check whether the current request matches the approved intent, the data classification, the environment, and the allowed action chain. That is where CSA MAESTRO agentic AI threat modeling framework is useful: it pushes teams to model tool misuse, cross-system chaining, and escalation paths before deployment.
- Use JIT credential provisioning for each task rather than reusable secrets.
- Bind access to workload identity, not just a prompt session or human owner.
- Evaluate policy at runtime with full context, including destination, data type, and action intent.
- Revoke tokens automatically on task completion or anomaly detection.
NHIMG research shows why this matters: the AI LLM hijack breach and Moltbook AI agent keys breach both illustrate how exposed or over-broad secrets turn agentic convenience into lateral movement risk. These controls tend to break down when agents are allowed to reuse static credentials across multiple tools because the compromise window becomes too wide to contain.
Where the Edge Cases and Failure Modes Usually Appear
Tighter control often increases operational overhead, requiring organisations to balance security benefit against latency, token management, and policy maintenance. That tradeoff is real, and there is no universal standard for intent-based authorisation yet. Best practice is evolving, especially for multi-agent pipelines where one agent calls another and the effective privilege chain becomes difficult to trace.
The biggest edge case is autonomous behaviour that is “correct” from the model’s perspective but wrong from the business perspective. An agent might optimise for success, continue retrying, pivot to alternative tools, or escalate through allowed APIs in ways no human reviewer anticipated. That is why the current consensus favours layered controls: restricted tool catalogs, per-task secrets, auditable prompts and actions, anomaly detection, and human approval for sensitive state changes. The OWASP Agentic AI Top 10 and MITRE ATLAS adversarial AI threat matrix both reinforce that agent behaviour can be exploited through tool abuse, prompt manipulation, and indirect escalation.
NHIMG’s survey data also shows the gap between confidence and control: only 44% of organisations have implemented any policies to manage their AI agents, despite 92% saying governance is critical. That is a strong sign that many environments are still relying on static IAM assumptions. The hardest cases are high-autonomy agents in cloud and infrastructure workflows, especially where static credentials, shared service accounts, and weak audit trails already exist. In those environments, least privilege is usually defined on paper but not enforced at runtime.
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 | A2 | Agent tool abuse and privilege escalation are central to this question. |
| CSA MAESTRO | T1 | MAESTRO models agent behaviour, tool chains, and runtime abuse paths. |
| NIST AI RMF | AI RMF governance is relevant because agents need runtime oversight and accountability. |
Assign ownership, monitor behaviour, and document controls for autonomous agent actions.
