Why do machine and AI identities make traditional PAM governance harder?

Why This Matters for Security Teams

Traditional PAM was designed to govern people: named users, predictable shifts, approval queues, and reviews that happen on a schedule. Machine and AI identities break that model because they are created programmatically, operate at machine speed, and often chain multiple services without a human session boundary. That makes entitlement drift, over-privilege, and secret reuse much harder to see in time.

This is why NHI governance has moved from “who approved access?” to “what is allowed right now, for this task, in this context?” Current guidance from the NIST Cybersecurity Framework 2.0 supports outcome-based access control, but it does not remove the operational challenge of non-human workloads that never follow human working patterns. NHIMG’s Top 10 NHI Issues and Lifecycle Processes for Managing NHIs both show the same pattern: control failures usually start with long-lived credentials and end with access no one can confidently inventory.

In the 2024 ESG Report: Managing Non-Human Identities, Oasis Security & ESG reported that 72% of organisations have experienced or suspect a breach of non-human identities. In practice, many security teams discover the problem only after a machine account or AI workload has already been used to move laterally or call sensitive APIs, rather than through intentional review.

How It Works in Practice

Effective PAM for machine and AI identities has to shift from standing entitlements to runtime control. That usually means issuing short-lived credentials only when a workload needs them, binding those credentials to a workload identity, and revoking them automatically when the task ends. For AI agents and autonomous systems, the challenge is sharper because the access path is not fixed in advance. The agent may decide which tool to call next, so static role maps can become either too restrictive or too permissive.

Practitioner guidance is converging on three controls. First, use workload identity as the primary identity primitive, such as SPIFFE or OIDC-based service tokens, so the system can prove what the workload is rather than trusting a shared secret. Second, apply intent-based or context-aware authorisation at request time, using policy-as-code engines such as OPA or Cedar to evaluate the task, destination, time, and environment. Third, keep secrets dynamic and ephemeral, with TTLs aligned to task duration instead of human review cycles.

• Issue credentials just in time for a single workflow or bounded task.
• Bind access to workload identity, not to a reusable shared account.
• Evaluate privilege at runtime, including API scope, data sensitivity, and destination service.
• Revoke or expire tokens automatically when the job completes or context changes.

These controls align with NIST’s outcome-driven model and help operationalise the governance themes in NHIMG’s Regulatory and Audit Perspectives. They also fit the broader guidance in the NIST Cybersecurity Framework 2.0, especially where access control must be continuously validated rather than periodically certified. These controls tend to break down when legacy platforms require persistent service accounts, because the application itself cannot yet tolerate short-lived tokens or per-task reauthentication.

Common Variations and Edge Cases

Tighter PAM for non-human identities often increases operational overhead, so organisations have to balance security gain against deployment friction and service reliability. That tradeoff is especially visible in legacy applications, batch jobs, and vendor-integrated workflows where a human operator cannot easily step in to refresh access. Current guidance suggests treating those cases as exceptions with compensating controls rather than accepting long-lived standing privilege as the default.

There is no universal standard for this yet, but best practice is evolving toward layered control. Shared service accounts should be reduced, not expanded. Where they still exist, they need strong segmentation, monitored usage, and tightly bounded secrets exposure. For AI agents specifically, the risk is not only credential theft but unexpected tool chaining, lateral movement, and privilege escalation during multi-step execution. That is why ai governance must be tied to the agent’s runtime intent, not just its registration record.

NHIMG’s research on the JetBrains GitHub plugin token exposure and the BeyondTrust API key breach reinforces a practical lesson: when secrets outlive the task, compromise becomes harder to detect and easier to reuse. In mature environments, the goal is not zero secrets, but fewer persistent ones and faster revocation when an identity behaves outside its expected context.

Related resources from NHI Mgmt Group

• Why do AI workflows make data governance harder than traditional applications?
• Why do AI copilots and agents make PII governance harder than traditional DLP does?
• Why do non-human identities make identity maturity harder to sustain?
• Why do AI agents make non-human identity governance harder?