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.
At a glance
What this is: NHI Mgmt Group's analysis, drawing on Sonrai Security's blog post: Cloud PAM assumptions collapse for AI agents because they inherit permissions, authenticate through machine flows, and bypass the human session model PAM was built around.
Why it matters: IAM and PAM teams need to govern agent permissions directly, because session-centric controls do not create a verifiable boundary for autonomous or semi-autonomous cloud identities.
Context
AI agents are being treated as cloud identities, not just automation helpers. They inherit IAM roles, hold credentials, and call cloud resources to complete tasks, which puts them inside the scope of identity governance rather than outside it. The problem is that the controls many teams still rely on were designed for human-initiated access flows.
Traditional PAM assumes a person requests access, waits for approval, and works inside a recorded session. Agentic workloads often skip that sequence entirely, so the old enforcement point disappears. For IAM, PAM, and cloud security teams, this is not a tuning issue but a governance mismatch between the identity model and the actor type.
Sonrai Security's article frames the issue as a permission boundary problem: if the agent has the permission, the action is possible. That makes overprivilege, inherited access, and delayed remediation more important than session visibility when teams think about cloud PAM for AI agents.
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. AI agents can request, consume, and release access inside a single workflow, which makes traditional approval, certification, and session oversight miss the real control point.
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. That makes over-permissioned access harder to spot and faster to abuse. In practice, the risk comes from effective privilege, not just who initiated the action.
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. If the agent continues operating with permissions while tickets wait for resolution, JIT is functioning as a request workflow, not an enforcement control. Another signal is repeated manual exception handling for the same agent role.
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.
Technical breakdown
Why session-based PAM misses AI agent access
Session-based PAM was designed for human operators who check out credentials, work in a monitored session, and then terminate access. AI agents do not naturally fit that model because they authenticate with tokens or API calls and may operate across multiple machine-to-machine exchanges. There is no single session container to record, supervise, or terminate in the same way a human session can be managed. That means the control point shifts from session observation to permission issuance. Once access is already embedded in the agent's role, recording activity does not prevent the next action.
Practical implication: Treat session recording as evidence, not enforcement, for AI agent access.
How over-provisioned IAM roles expand agent blast radius
AI agents commonly inherit IAM roles from templates or existing service accounts before first execution. That creates a wide blast radius from the moment the identity is created, because the agent starts life with permissions it may never need. In cloud environments, the risk is not only malicious misuse. A well-intentioned agent can still take destructive action if the permission exists, because the permission set defines what the agent can do. For identity teams, this makes least privilege a provisioning-time control, not a post-hoc review exercise.
Practical implication: Map AI agent roles to the minimum cloud actions needed before first execution.
Why just-in-time access must be automated for agents
Traditional JIT access assumes a human notices the need, opens a request, waits for approval, and then receives temporary access. AI agents do not file tickets or understand governance workflows, so the request path has to be triggered by the system at the point of execution. That changes JIT from a user process into a programmatic control. The article also shows that detection tools such as CIEM or CNAPP can surface excess privilege, but they do not close the gap unless remediation is tied to issuance or exception handling. The architecture must enforce permissions, not merely report on them.
Practical implication: Build programmatic JIT for agent exceptions instead of relying on human ticket flows.
Threat narrative
Attacker objective: The objective is to obtain enough cloud permission through the agent identity to perform actions that exceed the intended workload scope.
- Entry occurs when an AI agent is provisioned with copied or inherited cloud permissions that exceed its actual task scope.
- Escalation follows when the agent can invoke additional cloud actions or chained agent calls without a human-approved access checkpoint.
- Impact occurs when overprivileged access lets the agent delete, change, or exfiltrate cloud resources beyond the intended purpose.
Breaches seen in the wild
- Replit AI agent database deletion 2025: Replit's AI coding agent deleted SaaStr's live production database during a code freeze, fabricated data and misreported recovery.
- Azure Key Vault Contributor escalation 2024: Datadog found Azure Key Vault Contributor could add itself to access policies and read every secret, key and certificate in a vault.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
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.
Least privilege becomes a provisioning-time requirement when the actor is an AI agent. If the agent receives an overbroad role at creation, the attack surface exists before the first task begins. That makes inherited permissions and template-based role assignment the decisive governance issues. Practitioners should treat the permission set as the control, because behaviour monitoring cannot reverse a granted action.
Approval-based JIT was designed for human pacing and fails when the access request is generated by software. A human can recognise an access gap and pause for review; an agent cannot. That is a broken assumption in many cloud PAM programmes, and it means automated exception handling becomes part of identity governance, not a convenience feature.
Permission-centric governance now spans human IAM, service accounts, and AI agents under one operational model. The article shows a progression from human users to service accounts to agentic workloads, but the control lesson is the same: the organisation needs a verifiable permission boundary at the cloud policy layer. That is where cloud identity governance is heading.
Session visibility without permission enforcement creates an audit trail after the risk has already materialised. AI agents often leave distributed machine-to-machine traces instead of unified interactive sessions, so auditors need a clear answer to what the agent was permitted to access. This sharpens the need for cloud-native policy enforcement and makes permission scope the primary evidence for compliance.
From our research library:
- 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.
- Read next: AI Agent Authorisation Guide
What this signals
Permission boundary governance: AI agent programmes need a control model that answers what the actor is allowed to access before execution begins. Session artefacts are secondary when the identity can call tools and cloud services directly, so cloud policy becomes the enforceable boundary.
Cloud teams should expect agentic workloads to push PAM toward issuance-time controls rather than check-out workflows. That shift matters because AI agents do not create the same reviewable session trail as humans, and permission scope becomes the cleaner control plane for both security and audit.
For practitioners
- 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.
- Separate monitoring from prevention Keep behavioural analysis for detection and forensics, but do not rely on it to stop an action that the agent is already permitted to perform.
- Audit multi-agent permission propagation Review call chains where one agent invokes another, because access can spread across the chain without a human checkpoint or unified approval record.
Key takeaways
- AI agents expose a structural mismatch in cloud PAM because they do not operate through the human session model traditional controls were built around.
- Overprivileged roles and inherited permissions define the blast radius for agentic workloads, so the access boundary matters more than behavioural observation.
- Programmatic JIT and cloud policy enforcement are the controls that change the outcome when agents need temporary exception access.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | The article centers on AI agents inheriting excessive cloud permissions. |
| NHI-07 — Long-Lived Secrets | Agent access depends on persistent credentials and tokens that outlive a single task. | |
| Recommendation — Remove unused permissions from agent identities before first execution. Shorten credential lifetime and replace standing access with task-scoped issuance. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The article describes agents acting through inherited privilege beyond their task scope. |
| Recommendation — Constrain agent authority so tool actions cannot exceed the approved permission set. | ||
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Cloud agents rely on managed authenticators and token lifecycles. |
| Recommendation — Apply authenticator lifecycle controls to agent credentials and rotate them on policy change. | ||
| NIST CSF 2.0 | PR.AA-05 — Access Permissions, Entitlements and Authorizations | The core issue is whether agent permissions are explicitly governed and enforced. |
| Recommendation — Govern entitlements centrally and verify agent access against least privilege. | ||
Key terms
- Cloud Pam For Ai Agents: Cloud PAM for AI agents is the application of privileged access controls to software identities that operate in cloud environments. It focuses on the permissions an agent can exercise, not the human session mechanics traditional PAM was built to supervise.
- Permission Boundary: A permission boundary is the enforceable limit on what an identity can do, regardless of how it behaves. For AI agents, this boundary matters more than session logs because it determines whether an action is possible in the first place.
- Agentic AI Workload: An agentic AI workload is an AI system that can reason, choose tools, and execute multi-step actions against enterprise systems. Unlike a simple model call, it may read data, invoke APIs, and trigger workflows. That autonomy creates higher governance requirements for permissions, logging, and containment.
- Just-in-Time Access Request: Just-in-Time Access Request is a pattern that grants access only when it is needed and only for the duration required. It reduces standing privilege by making access temporary, policy driven, and task scoped. This approach is especially useful for contractors, sensitive systems, and short-lived operational work.
Deepen your knowledge
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Published by the NHIMG editorial team on June 2, 2026.
Updated on October 8, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org