Who should be accountable for cloud PAM when human, machine, and AI identities all have access?

Why This Matters for Security Teams

Cloud PAM becomes an accountability problem as soon as human users, service accounts, and AI-driven operational identities share the same blast radius. The mistake is treating each actor type as a separate policy universe, because privilege review, approval, and revocation then fragment across teams. NHI Management Group has documented how exposed credentials are often abused quickly once discovered in the wild, including findings summarized in LLMjacking: How Attackers Hijack AI Using Compromised NHIs. The same risk logic applies in cloud PAM: if the owner of the control plane is unclear, no one can answer who approved access, who should revoke it, or who is responsible when an agent chains privileges unexpectedly. Guidance from the OWASP Non-Human Identity Top 10 reinforces that non-human access cannot be managed as an afterthought to human IAM.

Practitioners often get this wrong by assigning ownership to whichever team created the credential, rather than the team accountable for privilege policy across the cloud environment. In practice, many security teams encounter privilege sprawl only after a shared secret, API token, or AI tool credential has already been used outside its intended scope.

How It Works in Practice

Accountability should be assigned to the identity and cloud control owners who define the policy, not to the actor class that consumes it. That means one accountable function must own access rules, approval logic, revocation workflows, and evidence collection across human, machine, and AI identities. Current guidance suggests using a common control model for all three, with exceptions documented only when the risk profile is materially different.

For cloud PAM, that usually means four operational layers:

• Identity ownership, where each workload, user, or agent has a named owner and a business purpose.
• Policy ownership, where access rules are defined centrally and expressed as code where possible.
• Credential ownership, where secrets are short-lived, rotated, and revoked on task completion.
• Review ownership, where approval and attestation happen in one place instead of per platform.

This matters because AI agents do not behave like static service accounts. They can chain tools, request new capabilities at runtime, and drift into privilege combinations that no manual role map anticipated. The right pattern is emerging toward runtime authorization, just-in-time issuance, and workload identity rather than long-lived static credentials. That aligns with the practical direction of the Ultimate Guide to NHIs and the control logic described in the OWASP Non-Human Identity Top 10.

For more mature programs, policy evaluation should happen at request time using context such as workload attestation, source, destination, and action. Best practice is evolving around workload identity primitives and real-time authorization, but there is no universal standard for this yet. These controls tend to break down when cloud privilege is administered by separate platform teams that cannot see the full identity graph, because revocation and exception handling become inconsistent.

Common Variations and Edge Cases

Tighter cloud PAM accountability often increases coordination overhead, requiring organisations to balance control strength against operational speed. The hard part is not declaring a single owner, but deciding how much of the decision tree should be centralized versus delegated.

One common edge case is a shared platform where human admins, CI/CD service accounts, and AI agents all touch the same APIs. In that environment, separate privilege logic can create blind spots, especially if the agent receives broad permissions through the same role used for automation. Another edge case is emergency access: if break-glass processes exist for humans but not for agents, incident responders may bypass normal approval paths and weaken the entire model. Current guidance suggests documenting those exceptions explicitly and time-boxing them.

There is also a governance tradeoff between treating AI agents like machines or like users. For most cloud PAM controls, they should be treated as autonomous workloads with workload identity and ephemeral authorization. The State of Secrets in AppSec highlights why this matters: leaked or fragmented secrets controls remain a persistent weakness, and that weakness compounds when AI systems can reuse or expose credentials across tools. NHI Management Group also documents the operational consequences of exposed cloud and identity artifacts in 52 NHI Breaches Analysis.

In practice, accountability fails when ownership is split by technology stack instead of by privilege decision, and the issue is usually discovered only after a shared credential or agent action has already crossed trust boundaries.

Related resources from NHI Mgmt Group

• How should security teams run access reviews for non-human identities?
• How should security teams govern non-human identities that have persistent access?
• How should security teams govern non-human identities in cloud environments?
• Who is accountable when access governance fails across human and machine identities?