Topic starter
26/08/2026 11:51 am
TL;DR: AI agents are already taking real actions inside CRM, ERP, and code environments, and Cakewalk’s session with Vanta argues that human IAM models fail because agents act faster, with broader privilege, and with less determinism than traditional users. The core issue is that access review and static RBAC assume stable, reviewable behaviour, which autonomous runtime action breaks.
Editorial analysis by NHI Mgmt Group, based on content published by Cakewalk: “AI Agents Have Too Much Access: Why Static Permissions Are a Security Risk [Webinar Summary]”.
Key questions
Q: What breaks when AI agents are treated like standard human users?
A: You lose visibility into effective permissions, expected behaviour, and real blast radius.
Q: Why do AI agents increase the risk of overpermissioning?
A: AI agents increase that risk because teams often expand scopes to unblock early use cases, then keep those permissions because the original need is hard to prove or remove.
Q: How do you know if AI agent authorization is actually working?
A: Authorization is working when each agent action can be tied to a current identity, a current policy, and a specific data or resource scope.
Practitioner guidance
- Audit agent credentials and scopes Inventory every deployed agent, the credentials it uses and the permissions attached to those credentials.
- Move enforcement to tool execution Place the authorization decision in front of the tool call, not after the model output.
- Define a selective escalation policy Escalate only genuinely ambiguous actions to human review, and keep routine actions on an automated path.
Bottom line: AI agent authorization is not a repackaged human IAM problem because the risky event is runtime tool execution, not just credential assignment.
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26/08/2026 11:51 am
Access review was designed for stable privilege, and that assumption collapses for AI agents. Review cycles depend on access persisting long enough to be observed, certified and removed. When an agent can acquire, use and discard capability inside one session, the control no longer sees the state it was built to govern. The implication is not more review forms, but a redefinition of what authorization evidence looks like for runtime actors.
A few things that frame the scale:
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: Should organisations separate agent authorization from human access governance?
A: Yes. They should keep the governance discipline aligned, but separate the operating model because agents need runtime checks on tool use, session context and decision logging. Human access governance alone does not address non-deterministic execution or prompt-driven scope drift.
👉 Read our full editorial: AI agent authorization is breaking human IAM assumptions