跳到正文
cccskills
免费在 GitHub 公开

gke-workload-security

Audit and strengthen security for applications running on GKE. Configure pod traffic restrictions, sandbox isolation, namespace security standards, and Secret Manager volume mounts to protect workloads at the application and namespace level.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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).

查看安装方法

适用场景

  • Auditing GKE security settings
  • Restricting communication within a namespace
  • Isolating risky pods and applying security standards
  • Mounting Secret Manager secrets as pod volumes

包含的文件(4)

  • SKILL.md7.6 KB
  • assets/default-deny-netpol.yaml235 B
  • assets/workload-identity-pod.yaml1.2 KB
  • scripts/audit_cluster.sh2.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

GKE Workload Security

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

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.

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). This is the recommended method for workloads to access Google Cloud APIs.

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

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

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.

中文简介正在准备中。当前显示英语简介。

  • Configure alerts for agent latency and error rates
  • Monitor rapid increases in token usage
  • Monitor safety triggers and permission denials
google/skills2.1万2026年10月17日 更新

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。

  • Choose a deployable model and supported hardware
  • Estimate hourly deployment costs before approval
  • Check deployment progress and test predictions
google/skills2.1万2026年10月17日 更新

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。

  • Listing endpoints and checking their configuration
  • Creating an endpoint before model deployment
  • Updating endpoint display names or labels
google/skills2.1万2026年10月17日 更新

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.

中文简介正在准备中。当前显示英语简介。

  • Build evaluation datasets from conversation logs
  • Generate simulated user scenarios
  • Choose scoring criteria for responses and tool use
google/skills2.1万2026年10月17日 更新

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.

中文简介正在准备中。当前显示英语简介。

  • Send a test prompt to Gemini
  • Call Llama or DeepSeek with the OpenAI SDK
  • Write code to call an existing custom endpoint
google/skills2.1万2026年10月17日 更新

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。

  • Moving a Gemini application to Google Cloud
  • Using Google Cloud credits for Gemini API calls
  • Unifying agent authentication and billing
google/skills2.1万2026年10月17日 更新

查看 google 的全部技能

报告此技能的问题