The post Cloud PAM for AI Agents: Why Traditional PAM Can't Protect Agentic Workloads appeared first on Sonrai | Enterprise Cloud Security Platform.

AI agents are cloud identities. They receive IAM roles at deployment, hold credentials, and access cloud resources to execute tasks. But unlike human users, they do this without session-based checkpoints, without manual approval steps, and sometimes with minimal human involvement. Traditional PAM was built around the assumption that a person initiates access. AI agents challenge that assumption entirely.

Cloud security teams have spent years extending identity governance outward: from human users to service accounts, from service accounts to CI/CD pipeline roles. AI agents are the next step in that progression. The access management challenges they introduce aren’t variations on problems PAM already solves. They require a fundamentally different approach.

Why AI Agents Are the Highest-Risk Cloud Identities

They inherit over-provisioned IAM roles from day one

AI agents are typically provisioned with IAM roles copied from existing service account templates, often without a permissions review before first execution. The result is a large attack surface from the moment the identity is created, before the agent has even executed a single task.

They execute without a human-approved access step

Every traditional PAM control assumes a human initiates access through a credential vault, a session request, or an approval workflow. AI agents don’t have this checkpoint. In multi-agent architectures, one agent invokes another, permissions propagate across the call chain, and no human approves what’s being accessed at any point.

Over-provisioned permissions define the blast radius

The security risk from an AI agent isn’t its behavior alone. It’s the permissions it holds. Overprivileged AI agents can be both maliciously exploited or just used negligently – the result is the same: it can take action beyond the intended purpose. Agents are relentless. If you ask them to do something, they will find a way to do it. Consider a well intended employee trying to use an agent to clean up some cloud resource costs. The agent goes ahead and deletes S3 buckets to free up some room. This wouldn’t be possible without that agent inheriting s3:DeleteBucket from somewhere.

Limitations of Traditional PAM for Agentic Workloads

Session-based controls have no enforcement point

Legacy PAM is built around session management: credential check-out, session recording, session termination. AI agents don’t initiate the kind of sessions PAM was built for. Instead, they authenticate with tokens, API calls, and communicate across multiple machine chains. There’s not the same information to manage, record, or terminate. That’s not a gap to be patched. It’s an architectural mismatch.

Approval-based JIT workflows don’t match agent execution speed

JIT access for humans depends on a request-review-approve sequence. A critical point is it assumes a human initiates the request. An agent can’t recognize an access gap and file a ticket. For agentic workloads, JIT request initiation has to be automated, triggered programmatically at the point of execution. That requires a fundamentally different model than traditional PAM was built to support.

CIEM and CNAPP tools identify risks but don’t enforce remediation

CIEM and CNAPP tools surface overprivileged IAM roles as findings. Unfortunately, the reality is there can be a meaningful gap between detection and remediation. During this window the agent continues operating with those permissions until a ticket is resolved.

Behavioral analysis is not a substitute for permission controls

Behavioral analysis is useful for investigation and forensics, but it is not a prevention control.

Behavioral and intent-based analysis can tell you what an agent is trying to do, but it has no bearing on what the agent is permitted to do. If the permission exists, the action is possible. Ideally, we want to prevent harmful action before it can happen. 

What Effective Cloud PAM for AI Agents Looks Like

Full identity inventory: human, non-human, and AI agent

Every AI agent operates with some form of cloud credentials, whether an IAM role, an API key, or inherited execution context, making it functionally an identity. Effective cloud PAM starts with a complete inventory across all identity types so permissions can be assessed and enforced consistently, not just for humans and service accounts. 

Least privilege enforcement at the org level

Least Privilege is the minimum set of permissions an AI agent needs to perform its function. When enforced, a compromised or malfunctioning agent is limited to what it’s permitted to access. Per-identity remediation doesn’t scale. The scalable model is org-level enforcement using cloud-native controls (like AWS SCPs) to remove unused privileged permissions across all identities in a single move. That’s how Sonrai’s Cloud Permissions Firewall works: one deployment, every identity in the org, including AI agent IAM roles.

JIT access for permission exceptions

Where a workflow requires a permission outside the agent’s least privilege scope, JIT access allows a request to be submitted, approved, and applied without giving standing access. Sonrai handles this via Slack or Microsoft Teams. Approval takes seconds, and the baseline role is restored when the task is complete. JIT workflows can be initiated automatically when an agent attempts an action – this is a much better fit for the way agents work, compared to traditional PAM Just-in-Time.

Regulated Environments Need a Verifiable Permission Boundary

Compliance audits require evidence that access was controlled within defined limits. For human users, session logs satisfy this. Challengingly, AI agents do not produce the same unified session records. Agent activity is spread across API calls and across several machine to machine communications meaning there is no unified container for an auditor to review.

This drives the necessary audit question to expand beyond “who accessed what” to include “what was the agent permitted to access.” That’s a permissions question, not a session question. When least privilege is enforced via cloud-native controls, the permitted boundary for every AI agent IAM role is active and verifiable at the cloud policy layer. This is an enforceable control, not a report, but offers auditors and compliance a clearly defined parameter for agents.

How Sonrai’s Cloud Permissions Firewall Secures AI Agent Identities

• Inventories every identity in the cloud environment, including AI agents, and identifies all unused and excessive privileged permissions.
• Deploys cloud-native controls in a single operation to block unused privileged permissions across all identities in the defined scope for ‘one-click least privilege‘.
• Permission exceptions are handled through Just-in-Time JIT workflows in seconds (Slack or Microsoft Teams) so agent tasks are unimpeded.
• DevOps and developer workflows are unaffected, as Sonrai operates at the cloud policy layer and does not touch CI/CD pipelines or deployment processes.

AI Agents Need Permissions Enforcement, Not Just Monitoring

Behavioral analysis, intent monitoring, and session recording were built for a human-speed world. AI agents operate at a fundamentally different pace and scale that traditional PAM can’t support. 

The only reliable security boundary for an agentic workload is its permission set. If the agent doesn’t have the necessary permissions, it can’t get access – regardless of autonomous attempts or manipulation. Sonrai’s Cloud Permissions Firewall enforces that boundary across every identity in the org, without new infrastructure, without breaking developer workflows, and without a manual remediation backlog.

Frequently Asked Questions

*** This is a Security Bloggers Network syndicated blog from Sonrai | Enterprise Cloud Security Platform authored by Tally Shea. Read the original post at: https://sonraisecurity.com/blog/cloud-pam-for-ai-agents-why-traditional-pam-cant-protect-agentic-workloads/