They should treat the combined stack as one identity system and map every machine-to-machine handoff, token, and delegated permission to an accountable owner. The key control is not the API gateway alone. It is the ability to trace, limit, and revoke access across the entire runtime path without relying on manual exception handling.
Why This Matters for Security Teams
API-heavy AI infrastructure often looks like ordinary service-to-service traffic until a token is reused, a scope is widened, or a microservice starts calling tools it was never meant to reach. That is why the stack should be governed as one identity system, not as isolated gateways and app teams. The practical risk is identity sprawl: every API key, service account, delegated token, and integration secret becomes part of the attack surface.
Current guidance suggests mapping ownership across the full runtime path, then enforcing least privilege at each handoff rather than trusting a perimeter control. This aligns with the NIST Cybersecurity Framework 2.0 emphasis on managed access and the NHI lifecycle discipline in Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs. NHIMG research shows that only 1.5 out of 10 organisations are highly confident in securing NHIs, which underscores how often API and microservice governance lags behind deployment speed.
In practice, many security teams discover excessive trust only after a service token has already been used to pivot into adjacent systems.
How It Works in Practice
Governance starts by inventorying every machine-to-machine relationship: which service calls which API, which token authorises that call, who owns the workload, and what business function the permission supports. The goal is not simply authentication. It is traceability, revocation, and proof that every delegated action is still justified at runtime.
Security teams usually get better results when they combine identity controls with runtime policy. That means short-lived credentials, narrow scopes, and continuous validation of context such as workload identity, destination service, environment, and request intent. For AI infrastructure, this often includes pairing API gateway controls with workload identity systems and policy-as-code enforcement so the decision is made at request time, not at design time. Standards such as NIST Cybersecurity Framework 2.0 support the governance model, while Top 10 NHI Issues captures the operational failures that most often appear in real environments.
- Assign one accountable owner per service identity, token family, and delegated permission.
- Use ephemeral secrets and rotate or revoke them automatically when a task completes.
- Log the full chain of custody for tokens, scopes, and downstream calls.
- Validate permissions at runtime, not only during deployment or annual review.
NHIMG data also shows that lack of credential rotation is the top cause of NHI-related attacks for 45% of organisations, which makes lifecycle automation a core control rather than an optional hardening step. These controls tend to break down in event-driven architectures with shared service accounts because attribution and revocation no longer map cleanly to a single runtime path.
Common Variations and Edge Cases
Tighter service-to-service control often increases operational overhead, requiring organisations to balance fast delivery against auditability and revocation speed. That tradeoff becomes sharper when AI workloads chain multiple microservices together, because each hop can introduce new permission requirements and new failure modes.
There is no universal standard for every environment yet, but current guidance suggests treating high-risk integrations differently from low-risk internal calls. External-facing APIs, third-party OAuth connections, and model-facing orchestration layers usually need stronger approval, shorter TTLs, and more frequent review than internal utility services. NHIMG research on Ultimate Guide to NHIs — Regulatory and Audit Perspectives is useful here because auditors increasingly want evidence that permissions can be traced from business owner to runtime action.
Edge cases also appear when a platform team centralises secrets but application teams retain local control over scopes, or when a gateway is hardened while backend service accounts remain over-privileged. In those cases, the visible control is stronger than the actual control plane. For teams dealing with exposed credentials and rapid abuse patterns, the DeepSeek breach is a reminder that secrets exposure can become an immediate runtime incident, not a theoretical governance gap.
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 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Covers rotation and lifecycle control for machine identities and secrets. |
| OWASP Agentic AI Top 10 | A-03 | AI-driven microservice calls need runtime authorization, not static trust. |
| NIST AI RMF | AI RMF applies to governing risk across autonomous AI-enabled infrastructure. |
Inventory service identities and automate short-lived credential rotation with clear revocation ownership.
