Plan and configure security across Google Kubernetes Engine clusters. Review access permissions, enable Secret Manager integration, and configure protections such as Shielded Nodes and Binary Authorization to strengthen the platform supporting your applications.
中文简介正在准备中。当前显示英语简介。
Plans, configures, and hardens platform-level Google Kubernetes Engine (GKE) cluster security. Covers cluster add-ons (Secret Manager enablement), RBAC hardening (disabling insecure bindings, audit tools), Binary Authorization, Secrets Encryption (--database-encryption-key), Security Posture (--security-posture), enabling Shielded Nodes, GKE Sandbox cluster enablement, GKE IAM roles, and cross-service authentication IAM patterns. Use when securing cluster control planes, hardening GKE RBAC, enabling Shielded Nodes, enabling GKE Sandbox runtime, enabling cluster-wide security add-ons, or managing GKE IAM roles. Don't use for Workload Identity (use gke-workload-identity) or workload-level security (SecretProviderClass, PSS, NetPol, gVisor pod runtimeClassName; use gke-workload-security).
安装前可以查看交给 AI 代理的指令内容。
This reference covers platform-level security hardening and cluster
configuration for Google Kubernetes Engine (GKE). For workload-level security
controls (such as Workload Identity Service Account bindings,
SecretProviderClass volume mounts, Network Policies, and Pod Security
Standards), refer to the gke-workload-security skill.
MCP Tools:
gke:get_cluster,k8s:check_k8s_auth,k8s:get_k8s_resource,k8s:apply_k8s_manifest,gke:update_cluster
| Setting | Golden Path Value | Day-0/1 | Notes |
|---|---|---|---|
workloadIdentityConfig.workloadPool | <PROJECT>.svc.id.goog | Day-0 | Workload Identity Federation for cluster pods |
secretManagerConfig.enabled | true | Day-1 | Google Secret Manager cluster add-on integration |
secretManagerConfig.rotationConfig | enabled: true, rotationInterval: 120s | Day-1 | Automatic secret rotation at the cluster level |
rbacBindingConfig.enableInsecureBindingSystemAuthenticated | false | Day-0 | Blocks legacy system:authenticated bindings |
rbacBindingConfig.enableInsecureBindingSystemUnauthenticated | false | Day-0 | Blocks legacy system:unauthenticated bindings |
nodeConfig.shieldedInstanceConfig.enableSecureBoot | true | Day-0 | Verifiable boot integrity |
nodeConfig.shieldedInstanceConfig.enableIntegrityMonitoring | true | Day-0 | Runtime integrity checks |
nodeConfig.workloadMetadataConfig.mode | GKE_METADATA | Day-0 | Blocks legacy metadata API, enforces Workload Identity |
| Private cluster + Dataplane V2 settings | See the gke-networking skill | Day-0 | Private nodes, private endpoint enforcement, ADVANCED_DATAPATH |
The golden path enables Secret Manager at the cluster level with automatic secret rotation.
# Verify Secret Manager is enabled on cluster
gcloud container clusters describe <CLUSTER_NAME> --region <REGION> \
--format="value(secretManagerConfig.enabled)" \
--quiet
# Enable if not already (Day-1 change)
gcloud container clusters update <CLUSTER_NAME> --region <REGION> \
--enable-secret-manager \
--secret-manager-rotation-interval=120s \
--quiet
Note: For configuring
SecretProviderClassmanifests and mounting secrets as volumes inside application deployments, see thegke-workload-securityskill.
The golden path disables insecure legacy RBAC bindings that grant broad access
to system:authenticated and system:unauthenticated groups.
# Verify insecure bindings are disabled
gcloud container clusters describe <CLUSTER_NAME> --region <REGION> \
--format="yaml(rbacBindingConfig)" \
--quiet
Best practices for RBAC:
system:authenticated
or system:unauthenticated.k8s:check_k8s_auth(parent="...", verb="list", resourceType="pods", namespace="...") (or kubectl auth can-i --list --as=<user>).k8s:get_k8s_resource(parent="...", resourceType="clusterrolebinding") (or kubectl get clusterrolebindings,rolebindings --all-namespaces).See the
gke-multitenancyskill for enterprise RBAC planning and https://docs.cloud.google.com/kubernetes-engine/docs/best-practices/rbac.md.txt
Not enabled in golden path by default but recommended for enforcing production image provenance across the cluster:
# Enable Binary Authorization
gcloud container clusters update <CLUSTER_NAME> --region <REGION> \
--binauthz-evaluation-mode=PROJECT_SINGLETON_POLICY_ENFORCE \
--quiet
Enabling verifiable node boot integrity and kernel isolation features at the cluster level:
# Enable Shielded Nodes on an existing cluster
gcloud container clusters update <CLUSTER_NAME> --region <REGION> \
--enable-shielded-nodes \
--quiet
# Enable GKE Sandbox (gVisor) runtime on an existing cluster
gcloud container clusters update <CLUSTER_NAME> --region <REGION> \
--enable-gke-sandbox \
--quiet
Note: To run workloads inside the gVisor sandbox, specify
runtimeClassName: gvisorin your Pod specs as detailed in thegke-workload-securityskill.
The five most common predefined IAM roles for GKE platform and cluster access:
| Role | Purpose | When to Use |
|---|---|---|
roles/container.admin | Full control over | Platform team admins |
| : : clusters and : managing cluster : | ||
| : : Kubernetes : lifecycle : | ||
| : : resources : : | ||
roles/container.clusterAdmin | Manage clusters but | Cluster operators |
| : : not project-level : who create/delete : | ||
| : : IAM : clusters : | ||
roles/container.developer | Deploy workloads | Application |
| : : (pods, services, : developers deploying : | ||
| : : deployments) : to existing clusters : | ||
roles/container.viewer | Read-only access to | Monitoring, |
| : : clusters and : auditing, or : | ||
| : : Kubernetes : read-only dashboards : | ||
| : : resources : : | ||
roles/container.clusterViewer | List and get | CI/CD pipelines that |
| : : cluster details : need cluster : | ||
| : : only : metadata : |
Principle of least privilege: Start with
roles/container.viewerorroles/container.developerand escalate only as needed. Avoid grantingroles/container.adminbroadly across teams.
service-<PROJECT_NUMBER>@container-engine-robot.iam.gserviceaccount.com):
Automatically created. Manages nodes, networking, and cluster operations on
your behalf. Do not remove or modify its permissions.roles/monitoring.metricWriter,
roles/logging.logWriter) and assign it at node pool creation time.roles/iam.workloadIdentityUser), refer to the
gke-workload-security skill.Common project-level IAM policy binding patterns for granting backend Google Service Accounts (GSAs) access to external Google Cloud services before linking via Workload Identity:
# Grant a GSA access to Cloud Storage objects
gcloud projects add-iam-policy-binding <PROJECT_ID> \
--member "serviceAccount:<GSA_NAME>@<PROJECT_ID>.iam.gserviceaccount.com" \
--role "roles/storage.objectViewer" \
--quiet
# Grant a GSA access to Cloud SQL databases
gcloud projects add-iam-policy-binding <PROJECT_ID> \
--member "serviceAccount:<GSA_NAME>@<PROJECT_ID>.iam.gserviceaccount.com" \
--role "roles/cloudsql.client" \
--quiet
# Grant a GSA access to Pub/Sub subscriptions
gcloud projects add-iam-policy-binding <PROJECT_ID> \
--member "serviceAccount:<GSA_NAME>@<PROJECT_ID>.iam.gserviceaccount.com" \
--role "roles/pubsub.subscriber" \
--quiet
## Resources
- [GKE Cluster Hardening Guide](https://cloud.google.com/kubernetes-engine/docs/how-to/hardening-your-cluster)
- [GKE RBAC Best Practices](https://cloud.google.com/kubernetes-engine/docs/best-practices/rbac)
- [Secret Manager Add-on for GKE](https://cloud.google.com/secret-manager/docs/secret-manager-managed-csi-component)
- [Binary Authorization on GKE](https://cloud.google.com/binary-authorization/docs/setting-up)
- [Shielded GKE Nodes](https://cloud.google.com/kubernetes-engine/docs/how-to/shielded-gke-nodes)
- [GKE Sandbox (gVisor)](https://cloud.google.com/kubernetes-engine/docs/how-to/sandbox-pods)
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Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.
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Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.
中文简介正在准备中。当前显示英语简介。
Manages Agent Platform endpoints, the stable URLs used to serve online model predictions. It supports listing, inspecting, creating, updating, and deleting endpoints, plus troubleshooting permission errors, quota limits, and resources that are busy.
中文简介正在准备中。当前显示英语简介。
Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.
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Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.
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Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.
中文简介正在准备中。当前显示英语简介。