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Gke Workload Security

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running security audits (`audit_cluster.sh`), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (`gVisor`), enforcing Pod Security Standards (`restricted` labeling) and pod securityContext, and mounting Secret Manager secrets via CSI (`SecretProviderClass`). Use when auditing workload security posture, isolating namespaces, applying pod security standards, or configuring network policies and secret volume mounts. Don't use for Workload Identity (use gke-workload-identity), cluster-wide control plane security, RBAC hardening, Binary Authorization, Shielded Nodes, or enabling platform-level GKE add-ons (use gke-platform-security instead).
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The gke-workload-security skill provides a structured approach to securing applications and namespaces running on Google Kubernetes Engine. It focuses on workload-specific security configurations, including network isolation, pod security standards, secret management, and runtime isolation. The skill includes an audit script to assess current cluster security posture and provides actionable workflows for implementing best practices such as Workload Identity, Network Policies, and gVisor sandboxing.

What it does

  • Audits GKE cluster security posture using a dedicated script.
  • Configures Workload Identity to allow Kubernetes Service Accounts to impersonate Google Service Accounts.
  • Implements Network Policies to control traffic flow and isolate namespaces.
  • Enables GKE Sandbox (gVisor) for enhanced kernel isolation of untrusted workloads.
  • Enforces Pod Security Standards using namespace labels.
  • Integrates Google Secret Manager via the CSI driver for secure secret mounting.

When to use it

  • When you need to assess the security configuration of your GKE workloads.
  • When you need to isolate namespaces using default-deny network policies.
  • When you need to enforce restricted pod security profiles.
  • When you need to securely mount secrets from Google Secret Manager into pods.
  • When you need to run untrusted workloads in a sandboxed environment.

Advantages

  • Provides clear, step-by-step procedures for common GKE security tasks.
  • Includes a diagnostic audit tool for quick security posture assessment.
  • Promotes the use of modern GKE security features like Dataplane V2 and CSI secret drivers.
  • Helps enforce the principle of least privilege through Workload Identity.
  • Offers standardized methods for pod isolation and security policy enforcement.

Usage examples

“Audit my GKE cluster named production-cluster in region us-central1.”
The agent executes the audit_cluster.sh script to report on the status of Workload Identity, Network Policies, and other security features.
“How do I isolate my web-app namespace using a default-deny network policy?”
The agent provides the command to apply the default-deny-netpol.yaml manifest to the specified namespace.
“Configure my pod to run in a gVisor sandbox.”
The agent provides the necessary runtimeClassName: gvisor configuration to add to the pod specification.
“Enforce the restricted pod security profile on the staging namespace.”
The agent provides the kubectl label command to apply the restricted security standard to the namespace.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/gke-workload-security/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gke-workload-security.zip -d .agents/skills

Documentation by Google

GKE Workload Security

Routing Note: For Workload Identity IAM and KSA/GSA bindings, use the gke-workload-identity skill. For cluster-level security flags (--database-encryption-key, --security-posture, RBAC, Shielded Nodes, Binary Authorization), use the gke-platform-security skill.

This skill provides workflows and best practices for securing GKE workloads. It covers security auditing, Identity and Access Management (Workload Identity), Network Security (Network Policies), and Node Security.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-workload-security)" and append agent-skills/1.0 (skill:gke-workload-security) to User-Agent headers.

Workflows

1. Security Audit

Assess the current security posture of your cluster using the provided audit script.

Prerequisites:

  • gcloud CLI authenticated.

  • jq command-line JSON processor installed.

Capabilities:

  • Checks for Workload Identity.

  • Verifies Network Policy is enabled.

  • Checks if Shielded Nodes are enabled.

  • Checks if Binary Authorization is enabled.

  • Checks for Private Cluster configuration.

Command:

scripts/audit_cluster.sh <cluster-name> <region> <project-id>
2. Configure Workload Identity

Workload Identity allows Kubernetes Service Accounts (KSAs) to impersonate Google Service Accounts (GSAs). Use this method when workloads require a dedicated GSA to access Google Cloud APIs; for most workloads, direct IAM principal binding (covered in the gke-workload-identity skill) is the recommended method.

Steps:

  1. Create Namespace and KSA:

    kubectl create namespace workload-identity-test-ns
    kubectl create serviceaccount <ksa-name> \
        --namespace workload-identity-test-ns
  2. Bind KSA to GSA:

    gcloud iam service-accounts add-iam-policy-binding <gsa-name>@<project-id>.iam.gserviceaccount.com \
        --role roles/iam.workloadIdentityUser \
        --member "serviceAccount:<project-id>.svc.id.goog[workload-identity-test-ns/<ksa-name>]"
  3. Annotate KSA:

    kubectl annotate serviceaccount <ksa-name> \
        --namespace workload-identity-test-ns \
        iam.gke.io/gcp-service-account=<gsa-name>@<project-id>.iam.gserviceaccount.com
  4. Verify Example Pod: Use existing asset assets/workload-identity-pod.yaml to test the configuration. Update the <ksa-name> in the file first.

    kubectl apply -f assets/workload-identity-pod.yaml -n workload-identity-test-ns
3. Implement Network Policies

Control traffic flow between Pods using Network Policies. By default, all traffic is allowed.

Enable Network Policy Enforcement:

gcloud container clusters update <cluster-name> \
    --update-addons=NetworkPolicy=ENABLED \
    --region <region>

[!NOTE] If your cluster uses Dataplane V2 (--enable-dataplane-v2), Network Policy enforcement is built-in and this step is not required (and may fail).

Apply Default Deny Policy: Isolate namespaces by denying all ingress and egress traffic by default.

Replace <target-namespace> with the namespace you want to isolate.

kubectl apply -f assets/default-deny-netpol.yaml -n <target-namespace>
4. GKE Sandbox (gVisor) Pod Isolation

Run untrusted workloads in a sandbox for extra kernel isolation. (Note: Enabling Shielded Nodes (--enable-shielded-nodes) and GKE Sandbox (--enable-gke-sandbox) at the cluster control plane level are platform-level actions covered in the gke-platform-security skill.)

Run a Sandboxed Pod: Add runtimeClassName: gvisor to your Pod spec:

apiVersion: v1
kind: Pod
metadata:
  name: sandboxed-pod
spec:
  runtimeClassName: gvisor
  containers:
  - name: app
    image: nginx
5. Pod Security Standards

Enforce security policies on namespaces using labels.

Enforce Restricted Profile:

kubectl label --overwrite ns <namespace> \
    pod-security.kubernetes.io/enforce=restricted \
    pod-security.kubernetes.io/enforce-version=latest

[!NOTE] Using latest ensures you use the policies corresponding to the cluster's current version. You can pin it to a specific version (e.g., v1.30) to lock down the namespace to policies of a specific release.

6. Secret Manager Integration (CSI Driver)

Mount secrets from Google Cloud Secret Manager directly as volumes in your pods.

Prerequisites: Secret Manager CSI driver must be enabled on the cluster.

Example SecretProviderClass:

apiVersion: secrets-store.csi.x-k8s.io/v1
kind: SecretProviderClass
metadata:
  name: my-secret-provider
spec:
  provider: gcp
  parameters:
    secrets: |
      - resourceName: "projects/<project-id>/secrets/my-secret/versions/latest"
        fileName: "my-secret-file"

Example Pod Spec excerpt:

spec:
  containers:
    - name: my-app
      volumeMounts:
        - name: secrets-store-inline
          mountPath: "/mnt/secrets"
          readOnly: true
  volumes:
    - name: secrets-store-inline
      csi:
        driver: secrets-store.csi.k8s.io
        readOnly: true
        volumeAttributes:
          secretProviderClass: "my-secret-provider"
7. Enable Network Policy Logging

If using GKE Dataplane V2, you can log allowed and denied connections.

Steps:

  1. Configure the NetworkLogging custom resource.

Example NetworkLogging Manifest:

apiVersion: networking.gke.io/v1alpha1
kind: NetworkLogging
metadata:
  name: default
spec:
  cluster:
    allow:
      log: true
      delegate: true
    deny:
      log: true
      delegate: true

This will log connection details to Cloud Logging.

Best Practices

  1. Least Privilege: Always use Workload Identity with minimal IAM roles. Avoid using Node default service accounts.

  2. Network Isolation: Use Network Policies to restrict Pod-to-Pod communication. Enable Network Policy Logging for visibility.

  3. Image Security: Use Binary Authorization to ensure only trusted images are deployed.

  4. Secret Management: Use Secret Manager CSI driver instead of default Kubernetes secrets for sensitive data.

  5. Pod Security: Enforce baseline or restricted Pod Security Standards on all non-system namespaces.

  6. Policy Enforcement: Consider using Policy Controller (Gatekeeper) to enforce custom security and compliance policies across the cluster.

Resources

Files

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "gke-workload-security", distributed by AutoGineer (https://autogineer.com/agent-skills).

The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.

Files added by AutoGineer

  • ATTRIBUTION.md: this file, written by AutoGineer

  • LICENSE: copied from LICENSE

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/gke-workload-security · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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AutoGineer is not affiliated with or endorsed by Google.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download gke-workload-security.zip