Legacy IAM and PAM often struggle because they were built for slower identity growth and more predictable access patterns. AI-driven environments create more identities, more machine-to-machine access, and more frequent entitlement changes. That increases governance overhead, weakens visibility, and makes manual reviews less reliable. Security teams need automation and lifecycle controls that can keep pace with the environment.
Why This Matters for Security Teams
Legacy IAM and PAM were designed around people, service accounts, and relatively stable application estates. AI-driven applications change that model by introducing more non-human identities, more frequent token issuance, and more delegated tool use across systems. The result is not just more accounts to review, but more decision points where access must be correct at runtime, not just at onboarding.
This is why current guidance increasingly points toward workload identity, short-lived credentials, and policy evaluated in context rather than static entitlement maps. The problem is visible in real-world AI abuse patterns documented in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research and in standards guidance such as the OWASP Agentic AI Top 10. In practice, many security teams discover entitlement drift only after an agent has already chained access across systems and made the review trail too noisy to trust.
How It Works in Practice
For AI-driven workloads, the main shift is from identity as a record in a directory to identity as a cryptographic proof of what the workload is doing right now. That is why frameworks such as NIST AI Risk Management Framework and the CSA MAESTRO agentic AI threat modeling framework emphasize runtime governance, not only provisioning workflows.
In practice, mature teams are moving toward:
- Workload identity for agents, often using SPIFFE or OIDC-backed tokens, so access is tied to the running workload rather than a shared secret.
- JIT credential issuance with short TTLs, so a token exists only for the task it supports and is revoked automatically when the task ends.
- Policy-as-code decisions at request time, where the system checks intent, data sensitivity, destination service, and risk signals before releasing access.
- Segmentation of tool access, so an agent that can query data cannot automatically write, deploy, or approve changes without a separate authorization path.
This operating model aligns with the lifecycle discipline described in Ultimate Guide to NHIs ? Lifecycle Processes for Managing NHIs and the control failures highlighted in Top 10 NHI Issues. The point is not to eliminate IAM or PAM, but to make them dynamic enough to keep pace with agents that can initiate tool calls, follow branching paths, and request new access mid-task. These controls tend to break down when agents operate across loosely governed SaaS, data, and code environments because the policy context is fragmented and the approval chain cannot keep up.
Common Variations and Edge Cases
Tighter control often increases operational overhead, requiring organisations to balance stronger containment against developer and platform friction. That tradeoff is real, especially when AI systems are embedded in customer-facing workflows, internal copilots, or multi-agent pipelines that require rapid tool switching.
Best practice is still evolving, but several edge cases are already clear. Shared API keys are especially risky because they blur attribution and defeat least privilege. Long-lived secrets are harder to justify for agents than for humans because autonomous systems can make many more requests per hour, which expands blast radius when compromise occurs. Some teams also assume PAM can solve the problem by adding approval gates, but that can become brittle when the agent needs per-request context rather than pre-approved static access. The emerging answer is a mix of short-lived credentials, context-aware authorization, and strong workload attestation, as reflected in the NIST Cybersecurity Framework 2.0 and the OWASP NHI Top 10.
In environments with legacy middleware, batch jobs, or vendor-managed integrations, these controls often fail because the platform cannot reliably distinguish human approval from machine-initiated delegation, which leaves revocation and audit trails incomplete.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Short-lived secrets and rotation are central to managing AI-driven NHI sprawl. |
| OWASP Agentic AI Top 10 | AIA-04 | Agentic workflows need runtime controls because behavior is dynamic and goal-driven. |
| CSA MAESTRO | TMC-02 | MAESTRO addresses threat modeling for autonomous agents and their tool chains. |
| NIST AI RMF | AI RMF governs accountability, measurement, and runtime risk management for AI systems. | |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access becomes harder as AI systems create more dynamic entitlements. |
Replace static AI credentials with ephemeral issuance, rotation, and automatic revocation.
