Topic starter
16/04/2026 6:39 pm
TL;DR: AI agents behave as cloud identities that inherit roles, credentials, and tool access, but traditional PAM assumes a human request, approval, and session model that agentic workloads do not follow, according to Sonrai Security. The control gap is architectural: security teams need permission-centric governance, not session-centric monitoring.
Editorial analysis by NHI Mgmt Group, based on content published by Sonrai Security: “Cloud PAM for AI Agents: Why Traditional PAM Can't Protect Agentic Workloads”.
Key questions
Q: What breaks when AI agents are managed with human PAM processes?
A: Human PAM processes assume stable sessions, visible request patterns, and access that persists long enough to be reviewed.
Q: Why do AI agents create more cloud access risk than human users?
A: AI agents can chain API calls quickly, interact with multiple services in one session, and operate without the familiar human signals that security tools expect.
Q: What signals show that JIT access is not working for agents?
A: A common signal is a growing gap between excess privilege findings and actual permission removal.
Practitioner guidance
- Inventory AI agent identities separately from service accounts Track every agent that can authenticate to cloud resources, including inherited execution contexts, so you can evaluate the actual permission set attached to each identity.
- Enforce least privilege at the cloud policy layer Use org-wide cloud controls to remove unused privileged permissions across agent roles before the first task executes, rather than waiting for per-role cleanup.
- Automate permission exception handling for agent tasks Trigger JIT access programmatically when an agent attempts an out-of-scope action, then restore the baseline role after the task completes.
Bottom line: AI agents expose a structural mismatch in cloud PAM because they do not operate through the human session model traditional controls were built around.
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This topic was modified 4 days ago by NHI Mgmt Group
08/10/2026 5:55 pm
Cloud PAM for AI agents is really a permission-boundary problem, not a session-management problem. Traditional PAM presumes a person asks for access, waits for approval, and operates inside a recordable session. AI agents bypass that assumption because they execute through machine credentials and chained calls. The implication is that cloud identity programmes must govern what the agent can do, not just what the operator can see.
A few things that frame the scale:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should teams use session recording or permission enforcement for AI agents?
A: Teams should prioritise permission enforcement. Session recording can help with investigation, but AI agents may not generate the kind of unified session evidence PAM was designed to capture. If the control goal is prevention, the permission boundary has to be active in the cloud policy layer before the agent acts.
👉 Read our full editorial: Cloud PAM for AI agents: why traditional PAM falls short