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Secure an AI inference gateway by treating caller authentication, permission to use a model or route, Kubernetes administration, network reachability, and runtime abuse controls as separate layers. Kubernetes RBAC protects Kubernetes API operations; it does not by itself decide which application user may call an inference endpoint. A defensible design verifies each caller, authorizes the requested action, limits the paths and volume of traffic, and records enough information to investigate misuse without exposing credentials or unnecessary sensitive content.
Start by defining what the gateway must protect
An inference gateway is a boundary between callers and model-serving infrastructure, but it is not the only security boundary. Map the identities and paths that can reach it before choosing controls.
- Callers: people, applications, services, and automated workloads that submit inference requests.
- Protected actions: invoking a model, selecting a deployment or route, accessing sensitive operations, and administering gateway configuration.
- Backends: model endpoints, internal services, and any infrastructure the gateway can reach.
- Administrative paths: gateway management interfaces, Kubernetes API access, and other control-plane services.
- Data and evidence: credentials, prompts, responses, identity claims, authorization decisions, and audit records.
Trace both the intended request path and indirect paths. For example, a workload that cannot call a model endpoint directly may still gain access through a gateway route or a delegated deployment capability. This map informs which layer should enforce each rule and what needs to be tested.
Authenticate callers, then authorize each inference action
Authentication answers “who is making this request?” Authorization answers “may that identity perform this action on this resource?” A valid token or API key establishes neither unrestricted model access nor permission to administer the gateway.
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Use an identity provider (IdP) or another supported identity system to establish caller identity. For a JWT-based setup, a product-specific Inference Gateway example has clients obtain a JWT from an identity provider and send it as a bearer credential in the Authorization header. That is an example, not a universal gateway standard. Configure the equivalent checks in the gateway you operate: verify the signature, issuer, expiry, and intended audience, and reject invalid credentials. The documented example returns HTTP 401 for invalid requests; confirm the behavior and configuration for your gateway version.
After authentication, apply an application-level policy to the requested model, route, deployment, tenant, and operation. Deny access by default where practical, and grant only the actions each identity needs. OWASP recommends enforcing access at multiple layers of an AI system, including the gateway, application, and model endpoint. This reduces reliance on any single policy point.
Use API keys as credentials, not as the access policy
An API key proves possession of a credential; it does not inherently express which models, routes, or administrative operations its holder may use. Associate each key with a distinct caller or workload and a narrowly scoped identity, role, or tenant policy. Avoid shared keys when individual credentials would allow clearer revocation and attribution.
Store and distribute keys safely
- Do not hardcode keys or tokens in source code, notebooks, container images, or client-side distributions.
- Keep credentials in managed secret storage or inject them through a protected deployment mechanism appropriate to the runtime.
- Restrict which people and workloads can retrieve each secret, and avoid copying it into build output or support artifacts.
- Never include raw credentials in request logs, traces, exception messages, or incident reports.
Define the credential lifecycle
Document who may issue a credential, what identity and permissions it represents, how it is rotated, who can revoke it, and how suspected leakage is handled. Revoke or replace a credential when it is exposed or no longer needed, and check for dependent workloads before routine changes. There is no universal key format, expiration period, or rotation cadence established here; set those according to the gateway, identity provider, operational risk, and recovery requirements rather than assuming a standard interval.
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Keep Kubernetes RBAC separate from model access control
Kubernetes authorization is evaluated after authentication. RBAC grants permissions by combining verbs, such as reading or modifying, with resources, and those permissions can be scoped to a namespace or across a cluster. Use roles that grant only required verbs on required resources, with namespace scope when it fits the workload. Separate routine inference access from permissions to deploy, configure, or administer the gateway.
These Kubernetes permissions govern operations against the Kubernetes API; they do not automatically determine whether an authenticated application user may invoke a particular model. Create an explicit inference policy for identities and their allowed tenants, models, routes, deployments, quotas, and sensitive operations. Keep the identity mapping between the application policy and Kubernetes service accounts clear so that a broad cluster permission does not silently become broad model access.
Review indirect capabilities as well as direct permissions. A seemingly narrow Kubernetes permission can sometimes enable powerful actions through deployments, service accounts, or other delegated resources. Test the effective permissions of users and workloads, not just the names of their assigned roles. Kubernetes guidance also recommends the Node and RBAC authorizers with NodeRestriction; apply the relevant controls for the deployed cluster and version.
Restrict network paths to the gateway and its dependencies
Expose only the listeners callers are meant to use, over TLS. Keep management interfaces, Kubernetes control-plane endpoints, model backends, metadata services, and other internal ports reachable only from the peers that need them. A public inference endpoint does not require a public administrative endpoint or publicly reachable backend.
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- Ingress: limit public or internal entry points to the intended gateway listeners; restrict management traffic to trusted administrative paths.
- Backend access: allow the gateway to reach only required model-serving services and ports, and prevent unrelated callers from bypassing gateway policy.
- Kubernetes traffic: use NetworkPolicies or equivalent controls to constrain pod ingress and egress. Restrict access to the cluster API server to trusted networks, and do not expose etcd or kubelet interfaces publicly.
- Metadata services: block workload access to cloud metadata endpoints unless the workload specifically requires it.
These are architecture-level controls, not a copy-and-paste port list. The correct allowlist depends on whether the gateway is public or internal, the cluster topology, and the CNI, cloud, ingress, and policy implementations in use. Validate actual network paths and failover behavior in the deployed environment.
Limit runtime abuse and retain useful audit evidence
Authentication and network controls do not prevent an authorized caller from sending excessive or costly traffic. Apply per-tenant limits for request rate, token consumption, concurrency, and spend. Set thresholds according to workload patterns and service objectives, then test what callers receive when a limit is reached. Rate limits should protect availability and cost without accidentally allowing one tenant to consume another tenant’s allocation.
Validate inputs and use abuse detection to identify anomalous traffic. Monitor for changes in caller identity, selected model, request volume or shape, and authorization failures. Treat repeated denials, unusual model switching, and sudden usage changes as signals for investigation rather than relying on a single threshold.
Keep gateway and inference records useful for security response: capture identity and relevant authorization-decision context, while redacting keys and tokens. Apply the organization’s privacy and retention rules to prompts and responses; detailed content logging is not automatically necessary to establish who made a request or why it was allowed. Enable Kubernetes audit logging where appropriate and securely archive its audit records.
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Choose enforcement points that fit the deployment
These approaches can be combined. A gateway can validate identity and enforce model policy while a service mesh limits service-to-service paths, for example. Compare the actual capabilities and failure behavior of the selected products rather than assuming that a component named “gateway,” “mesh,” or “API protection” provides every control.
| Approach | Where it fits | Questions to verify |
|---|---|---|
| Gateway-native policy | Request authentication, route decisions, and enforcement close to the inference entry point. | Does it validate the needed token claims and distinguish users, workloads, tenants, routes, and models? Can it enforce request, token, concurrency, and spend limits? |
| Identity-provider integration | Establishing caller identity and issuing credentials or tokens for the gateway to validate. | Are issuer and audience restricted, and are expiry and signature checked? How are identities mapped into inference permissions and revoked credentials? |
| Service-mesh controls | Restricting service-to-service connectivity and isolating gateway-to-backend paths. | Does the mesh complement rather than replace application-level model authorization? Are policies compatible with the current runtime and observable during failures? |
| Separate API-protection layer | Adding API-focused enforcement or visibility where the gateway and surrounding infrastructure do not meet requirements. | Does it integrate with the identity provider and runtime, enforce the required policy granularity, and provide useful audit and incident-response context without duplicating or conflicting with gateway controls? |
NIST SP 800-228 describes basic and advanced API controls at pre-runtime and runtime stages and advocates a risk-based approach; it does not prescribe one configuration for every gateway. Compare identity integration, policy granularity, network isolation, rate and spend enforcement, auditability, compatibility, operational complexity, and failure behavior against your specific risks.
Review the deployed system before relying on the design
- Inventory the paths: document callers, public and internal listeners, administrative interfaces, model backends, metadata access, and Kubernetes control-plane connections.
- Test identity validation: verify that valid credentials work and that invalid signatures, issuers, audiences, or expired tokens are rejected as intended.
- Test authorization boundaries: confirm that each caller can reach only its permitted tenant, model, route, and operation, including through indirect or delegated capabilities.
- Inspect credential handling: confirm secrets are injected through protected mechanisms, absent from logs and client distributions, and revocable through a documented process.
- Exercise network policy: verify allowed and denied ingress and egress paths, including administrative access, backend reachability, metadata services, and cluster interfaces.
- Exercise runtime safeguards: test tenant-specific rate, token, concurrency, and spend limits, and verify alerts for anomalous use and authorization failures.
- Review evidence and recovery: confirm audit records identify the relevant caller and decision, protect sensitive content and credentials, and can be retained and retrieved under incident procedures.
Repeat this review when routes, identity mappings, gateway versions, cluster policy tooling, or backend topology change. Security depends on the effective configuration and reachable paths, not only on the intended architecture.
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