AI gateways often centralise provider API keys, making them efficient for developers but highly valuable to attackers. If the gateway or its delivery pipeline is compromised, one identity surface can expose multiple downstream services. That is why gateway credentials should be governed like privileged non-human identities, with scoped access, independent rotation, and tight publishing separation.
Why This Matters for Security Teams
AI gateways are attractive because they reduce developer friction, but that same convenience concentrates privilege. A single gateway often holds provider API keys, routing logic, and policy enforcement for multiple downstream services, which means compromise can expose far more than one workload. That is why NHI Management Group treats gateways as privileged non-human identities, not just middleware. The risk is not theoretical: secrets sprawl, fast-moving AI integrations, and shared delivery pipelines create a high-value blast radius that attackers actively target.
NHIMG research on the State of Secrets in AppSec shows how brittle secrets management remains in practice, even before AI-specific risk is added. OWASP also flags non-human identity exposure as a core control gap in the OWASP Non-Human Identity Top 10. In practice, many security teams discover the gateway is over-privileged only after a token leak, pipeline compromise, or unexpected lateral movement has already expanded the incident.
How It Works in Practice
The safest way to think about an AI gateway is as a privileged control plane for machine access. It should not hold broad, long-lived credentials unless there is no alternative. Current guidance suggests separating publishing rights, runtime access, and secret administration so one compromise cannot authorize all three. That means the gateway should authenticate as a workload identity, while specific downstream actions are authorized at request time based on context, purpose, and policy.
In practice, that usually means three layers:
- Use workload identity, such as SPIFFE/SPIRE or OIDC-backed service identity, so the gateway proves what it is before any secret is issued.
- Issue short-lived, just-in-time secrets for a specific task, then revoke them automatically when the task ends.
- Evaluate policy at runtime with policy-as-code, rather than baking static allowlists into the gateway configuration.
This approach aligns with the Ultimate Guide to NHIs – Static vs Dynamic Secrets, which explains why TTL matters differently for machine identities than for human users. It also matches the operational direction in the 52 NHI Breaches Analysis, where exposed credentials repeatedly became the foothold for broader compromise. NIST’s Cybersecurity Framework 2.0 reinforces the need to identify, protect, detect, respond, and recover around critical identity assets rather than treating them as ordinary configuration.
The same logic applies to gateway delivery pipelines. If the build system, secrets manager, or deployment automation can publish production gateway credentials, then the gateway is effectively only as secure as the weakest CI/CD control. These controls tend to break down in fast-moving multi-tenant environments where multiple teams share the same gateway and secret rotation is manual.
Common Variations and Edge Cases
Tighter gateway control often increases deployment overhead, requiring organisations to balance velocity against reduced blast radius. That tradeoff becomes sharper when the gateway must broker access to many AI providers, because each provider may use different token formats, token lifetimes, and rate-limit models. There is no universal standard for this yet, so best practice is still evolving.
Some teams cache upstream API keys inside the gateway for performance. That can be acceptable only when the cache is short-lived, tightly scoped, and fully isolated from deployment credentials. Others front multiple agents through one gateway, which adds another risk layer because an agent can chain tools, retry requests, or pivot across services in ways that are hard to predict. For that reason, the Guide to the Secret Sprawl Challenge is especially relevant when AI gateways are embedded in sprawling application estates.
Security teams should also watch for a common edge case: a gateway that is secure in production but exposed in staging or developer environments. Those lower-trust environments often retain broader logging, weaker access control, and longer-lived tokens. That creates a path from non-production leakage to production privilege escalation, especially when the same secret is reused across environments.
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-02 | Addresses over-privileged non-human identities and exposed secrets. |
| OWASP Agentic AI Top 10 | A-03 | Agentic gateways need runtime authorization, not static trust. |
| CSA MAESTRO | M1 | Covers security architecture for AI orchestration and tool access. |
| NIST AI RMF | AI RMF supports risk governance for autonomous AI access paths. | |
| NIST CSF 2.0 | PR.AC-4 | Least privilege and access management are central to gateway hardening. |
Scope gateway secrets tightly, rotate them fast, and remove standing privilege from the control plane.
