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Before running an enterprise AI application in production on Kubernetes, secure the cluster’s control plane and workload identities, protect secrets, constrain network and deployment behavior, and plan capacity around real workload signals. These six guardrails are an operational synthesis of Kubernetes guidance—not an official Kubernetes checklist with this title. The right implementation depends on your cluster version, operating model, workload, and compliance requirements.
1. Make the platform and control plane production-ready
Production readiness starts beyond the application container. Decide who can access the Kubernetes API, what they can do, and how the cluster will remain available and support the expected workload. The production design needs to account for authentication and authorization, high availability for core services such as the API server and etcd, DNS capacity, and namespace resource limits.
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Choose an operating boundary
Set out which responsibilities belong to your platform team and which, if any, belong to a cloud provider or Kubernetes partner. Compare the options against your operational capacity and compliance requirements, including responsibility for control-plane availability, certificates, identity integration, observability, and incident response. A managed or turnkey option does not remove the need to understand who owns each operational task.
Plan for workload demand
Estimate the demand the cluster and its supporting services must handle, including DNS as workloads scale. Kubernetes production planning calls out workload resource demand and supporting services; it does not provide universal CPU, memory, GPU, or accelerator values for AI applications. Those requirements depend on the model, serving stack, hardware, and traffic profile.
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2. Give each workload a narrow identity
Use a dedicated service account for each workload that needs a Kubernetes identity rather than relying on the namespace’s default service account. If an application does not need to call the Kubernetes API, disable automatic service-account token mounting for that workload. Grant users and service accounts only the permissions their tasks require, scoped to a namespace when that scope is sufficient.
Review workload creation rights
Check who can create Pods and other workload resources, not only who can directly read Secrets. A user able to create a Pod may be able to arrange for it to mount namespace resources such as Secrets, ConfigMaps, or PersistentVolumes. Namespace separation is useful, but it should not be treated as a strong security boundary by itself.
3. Protect secrets from storage through application use
Kubernetes Secret objects are stored unencrypted in etcd by default unless encryption at rest is configured. Restrict access to the get, list, and watch verbs: listing Secrets can expose their contents. Limit mounts to the containers that need a value, and make sure application code neither logs confidential values nor sends them to untrusted parties.
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Base64-encoded Secret data is not encrypted and provides no additional confidentiality. Avoid committing Secret manifests containing sensitive values to source repositories. Where the application and platform support them, consider short-lived Secrets and audit access to sensitive values.
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4. Isolate workloads and constrain network paths
Apply Pod Security Standards that match each namespace and workload. Pod Security Admission has been stable since Kubernetes v1.25, according to Kubernetes documentation. Evaluate namespaces rather than leaving their security level unspecified, and aim for the Restricted standard where the workload’s needs allow it. Sensitive workloads may require stronger isolation options.
Allow only expected network traffic
Use NetworkPolicies to define expected ingress and egress, including careful handling of cloud metadata endpoints where relevant. First confirm that the cluster’s network implementation supports and enforces NetworkPolicy; a policy object alone does not establish that traffic is being restricted. Account for the actual application communication paths before tightening rules.
5. Check workload policy and image integrity before deployment
Use admission controls to validate workload requests before Kubernetes accepts them. Kubernetes ValidatingAdmissionPolicy can use CEL expressions to block, audit, or warn about non-compliant requests; one documented example is disallowing the latest image tag. The Kubernetes application security checklist also recommends scanning images before deployment and validating image signatures.
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Test admission rules for both false positives and false negatives, and review them as Kubernetes APIs evolve. A policy can prevent unsafe configurations, but an overly broad or incorrectly designed rule can also disrupt legitimate deployments. Confirm that the target cluster has the relevant admission capability configured and that the policy’s effect—block, audit, or warn—is intentional.
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6. Coordinate capacity, health checks, and scaling
Set resource requests so the scheduler can place Pods appropriately. Use limits and namespace quotas to protect the cluster from uncontrolled consumption, and include supporting services such as DNS in capacity planning. For AI workloads, determine CPU, memory, GPU, or accelerator needs using the actual model, serving stack, hardware, and traffic profile; Kubernetes guidance does not establish universal AI-specific sizing values.
Choose probes for the failure they detect
| Probe | Use it to | Operational effect |
|---|---|---|
| Startup | Allow a slow-starting application time to initialize. | Separates startup behavior from checks intended for an already-started application. |
| Readiness | Determine whether a Pod is ready to serve requests. | Keeps an unready Pod out of service traffic. |
| Liveness | Detect a condition that a restart can correct. | Can trigger restarts; an overly aggressive or poorly designed check can cause repeated restarts under load and add pressure to remaining Pods. |
Coordinate probe behavior with the Horizontal Pod Autoscaler (HPA). Startup CPU spikes and unstable readiness can distort scaling signals, so check how initialization and readiness transitions affect the metrics and behavior your scaling setup uses.
Scale against the measured bottleneck
Kubernetes supports horizontal scaling through HPA and vertical scaling through Vertical Pod Autoscaler (VPA). Choose a scaling approach based on the workload’s measured bottleneck and service objective; the Kubernetes autoscaling overview does not establish one recommended metric for AI inference services.
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Before committing to a platform or promoting a workload, compare the operational responsibilities, control-plane and etcd availability arrangements, identity and authorization integration, Secret encryption and access workflows, NetworkPolicy enforcement, isolation options, admission capabilities, and ownership of observability and incident response. Then confirm the workload can meet its own latency, throughput, and resource needs in that environment. These are implementation choices to verify for the target cluster, not a universal provider ranking or an AI sizing formula.
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