Topic starter
23/03/2026 4:05 pm
TL;DR: AI agents are increasingly executing actions across enterprise systems, but the security model built for human users and deterministic workloads cannot reliably govern goal-driven behavior, according to Token Security. Identity, intent, and lifecycle controls now define whether autonomous access stays bounded or turns into persistent risk.
Editorial analysis by NHI Mgmt Group, based on content published by Token Security: “Why AI Agent Security Must be Intent-Based”.
Key questions
Q: What breaks when AI agents are managed like ordinary machine identities?
A: What breaks is the assumption that access scope can be fully understood from provisioning data and quarterly review.
Q: Why do AI agents make least privilege harder to enforce?
A: AI agents can move across multiple services, make autonomous decisions, and trigger several machine-to-machine actions in one task.
Q: How can organisations tell whether an AI agent is acting outside its intended scope?
A: Organisations should look for behaviour that crosses expected tool boundaries, generates unusual credentials, or chains actions across systems that are not part of the original task.
Practitioner guidance
- Define agent purpose before access is issued Record the specific objective, allowed system scope, and prohibited actions for each AI agent before credentials are created or granted.
- Bind agent credentials to named ownership Assign a human owner for every service account, token, or role an agent can use, and require that owner to remain accountable across changes.
- Replace broad roles with task-scoped permissions Break agent access into narrow privileges aligned to the job the agent is actually performing, then remove any inherited creator-level access.
Bottom line: AI agent security breaks the assumption that access can be defined once and treated as stable through the life of the identity.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 6 days ago by NHI Mgmt Group
06/10/2026 4:28 pm
Static IAM assumptions collapse when the actor is goal-driven. Traditional access models presume that an identity’s purpose can be translated into a stable permission set at provisioning time. AI agents violate that premise because they decide how to pursue an objective at runtime, not just whether to invoke a pre-scripted workflow. The implication is that policy has to move from entitlement-only logic to behaviour-aware governance.
A few things that frame the scale:
- Gartner predicts that more than 50% of successful cyberattacks against AI agents through 2029 will exploit access control weaknesses.
A question worth separating out:
Q: Should organisations treat AI coding agents as part of IAM and PAM governance?
A: Yes, when those agents can act on code, data, or tools in ways that affect production risk. Their permissions should be scoped, reviewed, and audited like other privileged systems, especially when they interact with sensitive routes, secrets, or regulated data. The governance question is who can let the agent act, and under what policy.
👉 Read our full editorial: Intent-based security for AI agents: why identity now breaks