Cloud PAM for AI agents: why traditional PAM falls short

At a glance

What this is: This is an analysis of why traditional PAM does not fit AI agents, with the key finding that agentic workloads need permission enforcement rather than human-style session controls.

Why it matters: It matters because AI agents can inherit over-privileged cloud access from day one, so IAM teams need controls that bound what an agent can do before execution begins.

👉 Read Sonrai Security's analysis of cloud PAM for AI agents and least privilege

Context

AI agent identity risk is growing because autonomous workloads inherit cloud permissions without the human checkpoints that PAM was designed to enforce. In practice, that means an agent can receive an IAM role, authenticate with tokens or APIs, and begin acting long before a reviewer has any chance to intervene. For IAM and NHI teams, the core issue is not visibility alone, but whether the permission boundary is actually enforceable.

Sonrai Security frames the problem as an architectural mismatch between session-based PAM and agentic execution. That is broadly representative of the current market condition: many enterprise controls still treat access as a human workflow, while AI agents operate as machine identities with their own call chains and blast radius. The governance question is whether the organisation can limit what the agent is allowed to do, not whether it can record a session after the fact.

Key questions

Q: How should security teams implement JIT access for AI agents in cloud environments?

A: They should automate elevation at the moment a task requires extra privilege, then revoke it immediately after completion. The request, approval, and enforcement path must be machine-triggered, because agents cannot open tickets or wait like humans. The goal is narrow, reversible access that preserves least privilege without blocking execution.

Q: Why do AI agents complicate traditional PAM assumptions?

A: Traditional PAM assumes a human starts the session, requests access, and can be observed through a checkpointed workflow. AI agents use tokens, APIs, and chained machine calls, so there may be no session boundary for classic PAM to control. That makes permission enforcement more reliable than session supervision.

Q: What breaks when AI agents inherit over-privileged cloud roles?

A: The blast radius expands immediately, because the agent can perform any action the role permits, including destructive or data-sensitive operations. If the role is copied from a service account template, the agent may start with privileges that were never reviewed for autonomous use. Least privilege must be applied before first execution, not after an incident.

Q: When should organisations treat AI agent access as a governance issue?

A: They should do so as soon as an agent can act on cloud resources without a human in the loop. At that point, access is no longer just an operational convenience. It becomes an audit and accountability problem, because the organisation must prove what the agent was permitted to do.

Technical breakdown

Why session-based PAM fails for AI agents

Traditional PAM is built around a person checking out credentials, starting a session, and then ending that session when work is done. AI agents do not behave that way. They authenticate through tokens, API calls, and chained machine interactions, often with no explicit human request at the moment of access. That means there is no reliable checkpoint for approval, no clean session boundary to record, and no easy place to terminate access once an agent has already acted. The technical mismatch is not a missing feature. It is a different identity model.

Practical implication: Treat agent access as policy enforcement at the permission layer, not as a session you can supervise after it starts.

How over-provisioned IAM roles expand an agent's blast radius

AI agents are often provisioned from existing service account templates or inherited execution roles, which makes excessive privilege the default rather than the exception. If an agent receives permissions it does not need, every task expands the attack surface, whether the action is malicious, mistaken, or simply over-automated. In multi-agent setups, permission can also propagate across agent-to-agent calls, multiplying the blast radius without a human approval point. That makes least privilege a design requirement, not a post-deployment cleanup task.

Practical implication: Audit agent roles before first execution and remove any privilege that is not required for the exact task scope.

Why just-in-time access must be automated for autonomous workloads

Just-in-time access works for humans because a user can recognize a missing permission, request it, and wait for approval. Agents cannot do that in the same way. If a workflow needs temporary privilege, the request has to be triggered programmatically at the point of execution, with the baseline permission restored immediately after. This is why JIT for agentic workloads is closer to policy orchestration than to classic PAM ticketing. The control must be fast enough to match machine speed and narrow enough to preserve least privilege.

Practical implication: Use automated, task-scoped elevation for exceptions and restore the baseline role as soon as the task completes.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Salesloft OAuth token breach — hackers stole OAuth tokens to access Salesforce data via Salesloft.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Permission enforcement is the real security boundary for AI agents. Session logging, behavioural analysis, and approval workflows all matter, but none of them stop an agent from acting if it already holds the right privilege. NHI governance for agentic workloads therefore starts with constraining the permission set, then proving those constraints are active in the cloud policy layer. Practitioners should assume that any control that only observes the agent is insufficient.

AI agents create a new kind of identity blast radius. The risk is not just that an agent can do too much, but that its permissions can be copied, inherited, and propagated faster than teams can review them. That turns inherited access into durable trust debt. Organisations need to inventory every non-human identity, then enforce least privilege across all of them as a standing control.

JIT for agents changes from a human convenience to a governance requirement. If a task needs extra privilege, the access path must be deterministic, automated, and reversible. Otherwise, teams fall back into standing privilege by another name. Practitioners should redesign exception handling so temporary access is the norm for edge cases, not an afterthought.

Cloud PAM for AI agents shifts accountability back to policy owners. The question is no longer whether a tool can see the agent's activity. The question is whether the cloud policy boundary was intentionally defined, reviewed, and enforced before the agent was allowed to act. Security teams should treat this as a governance control with audit consequences, not just an operational preference.

From our research:

• 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
• For a broader control model, see Ultimate Guide to NHIs for lifecycle, visibility, and access governance patterns.

What this signals

Identity blast radius: the practical challenge is not whether AI agents can be governed, but whether their permissions can be bounded before execution. With 70% of organisations already granting AI systems more access than human employees, per the 2026 Infrastructure Identity Survey, agentic access is being normalised faster than governance is maturing.

The programme implication is clear: if your cloud controls still assume a human session, they will miss the main failure mode for agents. Align agent governance to NIST AI Risk Management Framework governance principles and use policy boundaries that can be audited at the identity layer.

Teams should expect more pressure to prove what an agent was permitted to access, not just what it did. That pushes security, compliance, and platform engineering toward a shared operating model, with the identity boundary treated as an enforceable control rather than a report after the fact.

For practitioners

• Inventory all AI agent identities Map every agent to its IAM role, API key, token, or certificate, then identify where permissions are inherited from templates or shared execution contexts. This is the only way to see the full agent blast radius before you reduce it.
• Remove unused privilege before first execution Review the permissions attached to agent roles before deployment and strip any access that is not required for the exact task boundary. Use org-level policy enforcement where possible so the default state is least privilege, not cleanup after drift.
• Automate task-scoped elevation for exceptions Trigger just-in-time access programmatically when an agent needs temporary privilege, then restore the baseline role immediately after the task ends. Keep the approval path short enough to match machine execution speed.
• Separate observation from prevention Use session logs and behavioural analytics for investigation, but do not confuse those controls with prevention. The preventive boundary should be the permission set itself, enforced in the cloud policy layer.

Key takeaways

• AI agents expose a governance gap because they operate as identities without human-style access checkpoints.
• Over-privileged roles create the dominant risk, since the permission set defines the agent's blast radius.
• Practitioners should move from session-centric PAM to permission-centric cloud policy enforcement for autonomous workloads.

Key terms

• AI Agent Identity: An AI agent identity is the set of credentials, roles, and permissions used by an autonomous software entity to access tools and resources. In cloud environments, it behaves like a non-human identity and must be governed with the same precision as service accounts or workload credentials.
• 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.
• Identity Blast Radius: Identity blast radius is the amount of damage an identity can cause if it is misused, compromised, or simply over-automated. For agents, it grows quickly when roles are inherited, copied, or left broader than the task requires.

Deepen your knowledge

Cloud PAM for AI agents is a core topic in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are redesigning access controls for autonomous workloads, it is a practical place to start.

This post draws on content published by Sonrai Security: Cloud PAM for AI Agents: Why Traditional PAM Can't Protect Agentic Workloads. Read the original.