Secure an AI inference gateway by checking who is calling, deciding what that identity may do, limiting which systems can reach the gateway and its backends, and enforcing runtime limits. Treat these as separate, layered controls: Kubernetes RBAC protects Kubernetes resources, but it does not by itself decide which application user may invoke a model or route.
Map the gateway’s security boundaries first
Before configuring controls, identify the assets and paths they must protect. An inference gateway typically sits between clients and model-serving backends, but its deployment may also include an identity provider, a management interface, Kubernetes services, cloud resources, and monitoring systems. A control that protects one path may leave another exposed.
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- Assets: model endpoints and deployments, prompts and responses, credentials, tenant data, gateway configuration, and audit records.
- Callers: end users, applications, batch jobs, internal services, and administrators. Distinguish human identities from workload identities where the chosen identity system supports it.
- Routes: public or internal inference endpoints, model-specific routes, health checks, and administrative endpoints. Record which callers need each route.
- Backends and dependencies: model-serving services, identity providers, secret stores, telemetry systems, and any cloud metadata or control-plane services reachable from the gateway or its workloads.
Use this map to decide where identity is verified, where model access is authorized, and which network paths are actually required. NIST SP 800-228, published June 27, 2025, and updated in the NIST record on March 13, 2026, frames API protection as lifecycle controls that can be applied incrementally according to risk. It does not prescribe one configuration for every gateway.
Authenticate the caller, then authorize the requested action
Authentication establishes which user or workload made a request. Authorization decides whether that identity may perform the specific operation: invoke a model, use a route, consume a tenant’s quota, or administer the gateway. A valid credential is not proof that every model or operation should be available to its holder.
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OWASP’s inference API guidance recommends authentication and authorization for inference APIs and protection across multiple layers of an AI system. Enforce the decision at the gateway and, where appropriate, at the application or model endpoint too. This reduces the chance that an alternate route or a compromised intermediate component bypasses the intended access rule.
Validate tokens against the intended identity provider
If the gateway accepts identity-provider-issued JWTs, verify the token signature and check its issuer, expiry, and audience. The Inference Gateway documentation describes this pattern: clients obtain JWTs from an identity provider and send them as bearer credentials in the Authorization header; its configuration guide says invalid requests receive HTTP 401. That is a product-specific example, not a universal gateway behavior. Confirm the equivalent checks, error behavior, and configuration in the gateway version you deploy, especially that issuer and audience restrictions prevent tokens intended for another service from being accepted.
Keep Kubernetes RBAC separate from model access control
Kubernetes authorization runs after authentication and governs operations against the Kubernetes API. RBAC rules combine verbs, such as reading or updating, with resources, such as pods or deployments; bindings can apply within a namespace or across the cluster. Use the narrowest verbs and resources required, and prefer namespace scope when it fits the task. Kubernetes also recommends the Node and RBAC authorizers with NodeRestriction.
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Those permissions do not express whether an application user may call a particular inference route. Define that separately in gateway or application policy, using the identities and attributes available in your deployment. Keep routine inference permissions distinct from administrative permissions. Review indirect capabilities as well as direct permissions: the Kubernetes documentation warns that seemingly limited access can enable more powerful actions through delegated resources, service accounts, or deployment changes.
Manage API keys and other credentials as secrets
API keys identify or admit a caller; they should not be treated as a complete authorization policy. Give each caller or workload a distinct credential where possible, and associate it with a narrow role, tenant, or other enforceable policy. Do not put keys or bearer tokens in checked-in source, notebooks, browser or mobile application distributions, support tickets, or logs.
Define a credential lifecycle
Document who can issue credentials, what each credential can access, how it is stored and delivered, how it is rotated or revoked, and who responds to suspected leakage. Use managed secret storage or protected deployment-time injection rather than embedding secrets in images or configuration repositories. Limit access to the secret store itself and review which workloads can retrieve each credential.
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There is no universal API-key format, expiration period, or rotation cadence established across gateways. Set these according to the selected gateway and identity provider, the exposure risk, and the organization’s operating requirements. Ensure you can revoke a leaked credential promptly and have a tested path for replacing it without leaving unauthorized access in place.
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Keep keys and bearer tokens out of gateway logs, traces, error reports, and audit events. Record enough context to investigate a decision—such as the authenticated identity or workload, requested route or model, authorization outcome, and relevant request identifier—without collecting credentials. Apply the organization’s privacy and retention rules to prompts and responses; security telemetry does not automatically justify retaining full inference content.
Restrict network reachability around the gateway
Expose only the listeners clients are meant to use, and use TLS for API traffic. Restrict management interfaces and model-backend ports to trusted administrative or service peers. A public inference endpoint does not imply that its management plane, backend services, cluster control plane, or supporting infrastructure should also be publicly reachable.
Control ingress and egress
On Kubernetes, use NetworkPolicies or equivalent controls to constrain which workloads can connect to the gateway and which destinations the gateway can reach. Policies need to match the actual cluster networking implementation and topology: the right allowlist differs for a public gateway, an internal-only service, and a multi-cluster deployment. Validate the resulting traffic paths rather than copying port rules from another environment.
Restrict the Kubernetes API server to trusted networks, and do not expose etcd or kubelet interfaces publicly. Block workload access to cloud metadata endpoints unless a specific workload requires it, and then limit that exception to the necessary workload and destination. OWASP’s Kubernetes guidance identifies sensitive cluster ports and advises restricting them to trusted networks.
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Set runtime limits and monitor for misuse
Authentication and route policy cannot by themselves prevent accidental overload or abuse by an otherwise authorized tenant. OWASP recommends per-tenant limits for requests, tokens, concurrency, and spend to reduce denial-of-wallet risk. Choose thresholds to fit workload patterns and service objectives, and define what happens when a limit is reached—such as rejection, throttling, or escalation—based on gateway capabilities and service requirements.
Validate inputs and apply rate limiting and abuse detection. Alert on meaningful changes, including a new caller identity, unusual model selection, sharp traffic-pattern changes, repeated authorization failures, or unexpected consumption. Make sure alerts lead to an actionable response, such as investigating a credential, narrowing a policy, or temporarily restricting a route.
Kubernetes recommends enabling audit logging and securely archiving audit records. Combine infrastructure audit events with gateway decisions so an investigation can relate resource changes to inference activity. Protect the records themselves, restrict access, and apply appropriate retention and privacy controls.
Choose where each control should live
A gateway-native policy, identity-provider integration, service-mesh control, and separate API-protection layer can overlap, but they solve different parts of the problem. Compare them against the actual identities, routes, and deployment paths that need protection rather than assuming one layer replaces the others.
| Approach | What to evaluate | Important boundary |
|---|---|---|
| Gateway-native policy | Whether it can authorize the required user, workload, tenant, route, and model combinations, and enforce rate, token, concurrency, or spend limits. | Confirm supported policy granularity and what happens if policy evaluation fails. |
| Identity-provider integration | Token issuance and validation, including signature, issuer, expiry, and audience checks; identity claims available to authorization rules. | Successful token validation authenticates a caller; it does not alone grant every model or route. |
| Service-mesh controls | Whether the mesh can provide the required service-to-service identity and network segmentation for the deployed runtime. | Verify that end-user or tenant-level model authorization is enforced somewhere appropriate, not inferred from service connectivity alone. |
| Separate API-protection layer | Fit with the current gateway and runtime, visibility into requests and decisions, and support for the limits and abuse signals required. | Assess added operational complexity, duplicated policy, and failure behavior. |
For each option, verify compatibility with the existing infrastructure, auditability, incident-response visibility, and the operational consequences of a dependency outage. NIST SP 800-228 describes basic and advanced controls at pre-runtime and runtime stages and advocates a risk-based approach; the suitable division of responsibilities depends on the deployment.
Review the deployed configuration before release
Use a deployment review to check effective behavior, not just the intended policy. Test with representative identities and paths in the selected gateway, identity provider, Kubernetes release, CNI, and ingress setup.
- Identity: Confirm that missing, expired, wrongly issued, or wrong-audience tokens are rejected, and that valid tokens map to the expected caller identity.
- Authorization: Verify allowed and denied access for each relevant tenant, model, route, and administrative operation. Check direct access and paths enabled through Kubernetes resources or delegated identities.
- Credentials: Confirm that secrets are stored and injected through approved mechanisms, access is restricted, revocation works, and logs do not expose keys or tokens.
- Network: Trace intended client-to-gateway and gateway-to-backend paths; check that management interfaces, cluster endpoints, sensitive ports, and metadata services are not reachable from unintended sources.
- Runtime: Exercise configured request, token, concurrency, and spend controls, and confirm that limit events and anomalous behavior produce useful alerts.
- Audit: Check that gateway decisions and Kubernetes audit records are available to authorized responders, securely retained, and appropriately redacted.
- Failure behavior: Determine what happens when the identity provider, policy service, secret store, logging pipeline, or rate limiter is unavailable. Ensure the behavior matches the risk of the affected route.
Re-run the relevant checks after changes to gateway versions, identity claims, routes, cluster networking, or authorization policy. Security depends on the effective combination of those components, not on the presence of an RBAC role, API key, or network policy in isolation.
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