Why do service accounts and AI agents break traditional PAM models?

Traditional PAM assumes privilege is attached to a human account and can be protected by vaulting and session recording. Service accounts and AI agents break that assumption because they can operate continuously, generate actions at machine speed and persist beyond a normal login session. The control problem shifts from credential storage to runtime authorisation.

Why This Matters for Security Teams

Traditional PAM was built for human operators with bounded sessions, predictable work hours, and approval flows tied to a named person. Service accounts and AI agents do not behave that way. They run unattended, call APIs directly, chain tools, and may continue acting after the original task has changed. That makes vaulting alone insufficient, because the real risk is not just who can retrieve a secret, but what the workload can do once it has it.

For AI agents, the problem is sharper: runtime behaviour is goal-driven and can shift in response to new prompts, new data, or tool outputs. Current guidance from NIST AI Risk Management Framework and OWASP Agentic AI Top 10 treats that variability as a governance issue, not just an identity issue. NHIMG research shows why that matters in practice: in AI Agents: The New Attack Surface report, 80% of organisations said their AI agents had already performed actions beyond intended scope. In practice, many security teams discover this only after an agent has already accessed data, called a tool, or disclosed a credential rather than through intentional policy design.

How It Works in Practice

The control shift is from static privilege assignment to runtime authorisation. A service account or agent should not carry broad, long-lived access just because it may need it someday. Instead, it should prove its workload identity at execution time, request only the capability needed for the current task, and receive short-lived access that expires automatically. That is why workload identity primitives such as SPIFFE/SPIRE and short-lived OIDC tokens are increasingly preferred over shared passwords or static API keys.

For autonomous workloads, best practice is evolving toward intent-based authorisation. Rather than asking only “is this account allowed to reach this system?”, policy should also ask “is this agent allowed to perform this action in this context, for this purpose, right now?” That usually means policy-as-code evaluated at request time through engines such as OPA or Cedar, with signals from task context, data sensitivity, tool risk, and recent behaviour. This is also where CSA MAESTRO agentic AI threat modeling framework and OWASP NHI Top 10 are useful: they both push teams to model the agent, its tools, and its escalation paths rather than just the credential store.

• Issue just-in-time credentials per task, not reusable standing access.
• Bind secrets to workload identity, not to a human-derived role alone.
• Keep tokens short-lived and revoke them when the task ends or the agent deviates.
• Log the action, context, tool, and decision so incident responders can reconstruct the chain.

These controls tend to break down when the agent operates across many SaaS platforms with weak token revocation, because the runtime decision can be correct while the downstream API permissions remain permanently broad.

Common Variations and Edge Cases

Tighter runtime control often increases orchestration overhead, requiring organisations to balance faster automation against more policy maintenance and stronger observability. That tradeoff is especially visible in service-account-heavy environments where older jobs, integrations, and batch processes still rely on static credentials.

One common exception is a low-risk backend job with no external tool access and a narrow, fully deterministic function. Even there, current guidance suggests reducing standing privilege where possible, because “low-risk” jobs often become high-risk after a code change or pipeline expansion. Another edge case is the human-in-the-loop agent: if approval is embedded too late in the workflow, the agent may already have retrieved sensitive data or executed an unsafe pre-step before review occurs.

There is no universal standard for this yet, but the direction is clear across NIST AI Risk Management Framework, MITRE ATLAS adversarial AI threat matrix, and NHIMG research such as Ultimate Guide to NHIs — 2025 Outlook and Predictions: service accounts and agents need identities that are bound to workload, scope, and time, not just stored in a vault. In mature environments, the hardest problem is not initial access but preventing an agent from reusing valid privilege after its original purpose no longer applies.

Related resources from NHI Mgmt Group

• Why do AI agents create more exposure than traditional service accounts?
• Why do AI agents create more audit risk than traditional service accounts?
• When do AI agents become a bigger risk than traditional service accounts?
• Why do AI agents break traditional identity and access management models?