How should security teams centralise AI model access without losing identity visibility or breaking developer workflows?

Why This Matters for Security Teams

Centralising AI model access is not just a cost or architecture decision. It is an identity-control problem. When developers can call model providers from notebooks, apps, CI pipelines, and agent frameworks using scattered API keys, security teams lose visibility into who invoked what, from where, and for which workload. That creates weak audit trails and makes revocation slow, especially when a key is embedded in code or shared across teams. Guidance from the OWASP Non-Human Identity Top 10 and NHIMG research on the Ultimate Guide to NHIs both point to the same operational issue: identity sprawl creates control gaps faster than teams can patch them. In the NHIMG data set, 96% of organisations store secrets outside secrets managers in vulnerable locations, which is exactly how model access becomes invisible.

In practice, many security teams only discover the exposure after a leaked key or overbroad model token has already been used across multiple environments.

How It Works in Practice

The cleanest pattern is to place a policy enforcement point in front of model providers and treat that gateway as the only approved path. Users and services authenticate to the gateway once, then the gateway issues or brokers short-lived access to approved models based on identity, workload, and context. That preserves developer flow while giving security teams one place to log, rate-limit, revoke, and inspect requests. For AI workloads, this should be paired with workload identity, not just user identity, so the request can be tied to a service, pipeline, or agent instance rather than a shared credential.

This is where NIST SP 800-53 Rev. 5 Security and Privacy Controls helps frame the control objectives: enforce least privilege, maintain audit logs, and restrict credential exposure. For model access, current best practice is evolving toward policy-as-code at request time, using context such as environment, user role, data sensitivity, and model tier. In many cases, the gateway should also mint ephemeral tokens or exchange identity assertions so downstream providers never see long-lived developer keys. That reduces blast radius if a token leaks and makes it easier to revoke access without redeploying every application.

• Authenticate once at the gateway, not separately at each model provider.
• Bind each request to a user, service, or agent identity for auditability.
• Issue short-lived credentials or scoped tokens per session or task.
• Enforce policy at runtime so access decisions reflect current context.
• Log prompts, model targets, and approval decisions in one control plane.

NHIMG’s NHI Lifecycle Management Guide is especially relevant here because the operational burden is not just initial provisioning. Keys, service identities, and delegated tokens all need expiration, review, and offboarding. These controls tend to break down in multi-tenant developer platforms where teams can bypass the gateway to speed up experimentation, because convenience paths quickly become the shadow access path.

Common Variations and Edge Cases

Tighter gateway control often increases integration overhead, requiring organisations to balance developer speed against stronger identity governance. That tradeoff becomes visible in data science notebooks, local testing, and legacy services that were built to call model APIs directly. Current guidance suggests treating those as exceptions with explicit compensating controls, not as permanent carve-outs. If the platform supports it, federate identity through OIDC, service mesh, or workload identity so teams can keep familiar tooling while security retains traceability.

There is no universal standard for model access brokering yet, so organisations should avoid assuming every provider will support the same token exchange or policy model. Some teams will need a thin proxy, others a full API gateway, and agentic workflows may need additional guardrails for tool chaining and escalation. The practical question is not whether developers can still use their preferred SDKs, but whether those SDKs are forced through a governed path. NHIMG’s Top 10 NHI Issues highlights how often visibility fails when access is fragmented, and that same pattern appears in model platforms when teams create direct-provider shortcuts. In highly distributed environments, this guidance breaks down when local experimentation, third-party plugins, or unmanaged agent frameworks can reach the provider without passing through the central policy point.

Related resources from NHI Mgmt Group

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