securing-kubernetes-on-cloud▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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This skill covers hardening managed Kubernetes clusters on EKS, AKS, and GKE by implementing Pod Security Standards, network policies, workload identity, RBAC scoping, image admission controls, and runtime security monitoring. It addresses cloud-specific security features including IRSA for EKS, Workload Identity for GKE, and Managed Identities for AKS.
| name | securing-kubernetes-on-cloud |
| description | 'This skill covers hardening managed Kubernetes clusters on EKS, AKS, and GKE by implementing Pod Security Standards, network policies, workload identity, RBAC scoping, image admission controls, and runtime security monitoring. It addresses cloud-specific security features including IRSA for EKS, Workload Identity for GKE, and Managed Identities for AKS. ' |
| domain | cybersecurity |
| subdomain | cloud-security |
| tags | - kubernetes-security - eks - aks - gke - pod-security-standards - container-runtime |
| version | 1.0.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.IR-01 - ID.AM-08 - GV.SC-06 - DE.CM-01 |
Securing Kubernetes on Cloud
When to Use
- When deploying new managed Kubernetes clusters in production with security requirements
- When hardening existing EKS, AKS, or GKE clusters after a security audit or pentest finding
- When implementing workload identity to eliminate static cloud credentials in pods
- When enforcing pod security policies across namespaces to prevent container escapes
- When integrating runtime security monitoring for detecting container-level threats
Do not use for non-Kubernetes container deployments like ECS Fargate or Azure Container Instances, for application-level security within containers (see securing-serverless-functions), or for CI/CD pipeline security (see implementing-cloud-devsecops).
Prerequisites
- Managed Kubernetes cluster provisioned on EKS, AKS, or GKE with admin access
- kubectl configured with cluster admin credentials
- Familiarity with Kubernetes RBAC, namespaces, and security contexts
- Container network interface plugin supporting network policies (Calico, Cilium)
Workflow
Step 1: Enforce Pod Security Standards
Apply Pod Security Admission labels at the namespace level to enforce the Restricted profile in production namespaces. Pod Security Policies were removed in Kubernetes v1.25 and replaced with Pod Security Admission.
# Production namespace with restricted Pod Security Standard
apiVersion: v1
kind: Namespace
metadata:
name: production
labels:
pod-security.kubernetes.io/enforce: restricted
pod-security.kubernetes.io/enforce-version: latest
pod-security.kubernetes.io/audit: restricted
pod-security.kubernetes.io/warn: restricted
---
# Staging namespace with baseline enforcement
apiVersion: v1
kind: Namespace
metadata:
name: staging
labels:
pod-security.kubernetes.io/enforce: baseline
pod-security.kubernetes.io/audit: restricted
pod-security.kubernetes.io/warn: restricted
# Pod spec compliant with restricted profile
apiVersion: v1
kind: Pod
metadata:
name: secure-app
namespace: production
spec:
automountServiceAccountToken: false
securityContext:
runAsNonRoot: true
runAsUser: 1000
fsGroup: 1000
seccompProfile:
type: RuntimeDefault
containers:
- name: app
image: company/app:v2.1@sha256:abc123...
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
capabilities:
drop: ["ALL"]
resources:
limits:
cpu: "500m"
memory: "256Mi"
requests:
cpu: "100m"
memory: "128Mi"
Step 2: Configure Cloud-Native Workload Identity
Eliminate static cloud credentials in pods by binding Kubernetes service accounts to cloud IAM roles.
# EKS: IAM Roles for Service Accounts (IRSA)
eksctl create iamserviceaccount \
--cluster production-cluster \
--namespace production \
--name web-app-sa \
--attach-policy-arn arn:aws:iam::123456789012:policy/WebAppS3ReadOnly \
--approve
# GKE: Workload Identity
gcloud iam service-accounts create web-app-sa \
--project=my-gcp-project
gcloud iam service-accounts add-iam-policy-binding \
[email protected] \
--role roles/storage.objectViewer \
--member "serviceAccount:my-gcp-project.svc.id.goog[production/web-app-sa]"
kubectl annotate serviceaccount web-app-sa \
--namespace production \
iam.gke.io/gcp-service-account=web-app-sa@my-gcp-project.iam.gserviceaccount.com
# AKS: Azure AD Workload Identity
az identity create --name web-app-identity --resource-group production-rg
az identity federated-credential create \
--name web-app-federation \
--identity-name web-app-identity \
--resource-group production-rg \
--issuer "$(az aks show -n production-cluster -g production-rg --query oidcIssuerProfile.issuerUrl -o tsv)" \
--subject system:serviceaccount:production:web-app-sa
Step 3: Implement Network Policies
Deploy network policies to restrict pod-to-pod communication following the principle of least privilege. By default, Kubernetes allows all pods to communicate with each other.
# Default deny all ingress and egress in production namespace
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: default-deny-all
namespace: production
spec:
podSelector: {}
policyTypes:
- Ingress
- Egress
---
# Allow web-app to receive traffic from ingress controller only
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: allow-ingress-to-web
namespace: production
spec:
podSelector:
matchLabels:
app: web-app
policyTypes:
- Ingress
ingress:
- from:
- namespaceSelector:
matchLabels:
name: ingress-nginx
ports:
- protocol: TCP
port: 8080
---
# Allow web-app to connect to database only
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: allow-web-to-db
namespace: production
spec:
podSelector:
matchLabels:
app: web-app
policyTypes:
- Egress
egress:
- to:
- podSelector:
matchLabels:
app: postgres
ports:
- protocol: TCP
port: 5432
- to:
- namespaceSelector: {}
podSelector:
matchLabels:
k8s-app: kube-dns
ports:
- protocol: UDP
port: 53
Step 4: Configure RBAC with Least Privilege
Scope Kubernetes RBAC roles to specific namespaces and resources. Avoid ClusterRoleBindings for non-administrative users.
# Developer role scoped to specific namespace
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
name: developer-role
namespace: staging
rules:
- apiGroups: [""]
resources: ["pods", "pods/log", "services", "configmaps"]
verbs: ["get", "list", "watch"]
- apiGroups: ["apps"]
resources: ["deployments"]
verbs: ["get", "list", "watch", "update", "patch"]
# Explicitly deny secrets access
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: developer-binding
namespace: staging
subjects:
- kind: Group
name: developers
apiGroup: rbac.authorization.k8s.io
roleRef:
kind: Role
name: developer-role
apiGroup: rbac.authorization.k8s.io
Step 5: Deploy Image Admission Controls
Use admission controllers to enforce that only signed images from trusted registries are deployed. Implement OPA/Gatekeeper or Kyverno for policy enforcement.
# Kyverno policy: require images from approved registries
apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
name: restrict-image-registries
spec:
validationFailureAction: Enforce
rules:
- name: validate-registries
match:
any:
- resources:
kinds: ["Pod"]
validate:
message: "Images must come from approved registries"
pattern:
spec:
containers:
- image: "123456789012.dkr.ecr.us-east-1.amazonaws.com/* | gcr.io/my-gcp-project/*"
---
# Kyverno policy: require image digest (no mutable tags)
apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
name: require-image-digest
spec:
validationFailureAction: Enforce
rules:
- name: require-digest
match:
any:
- resources:
kinds: ["Pod"]
validate:
message: "Images must use digest references, not tags"
pattern:
spec:
containers:
- image: "*@sha256:*"
Step 6: Enable Runtime Security Monitoring
Deploy runtime security tools to detect anomalous behavior inside containers including process execution, file system modifications, and network connections.
# Deploy Falco for runtime threat detection
helm repo add falcosecurity https://falcosecurity.github.io/charts
helm install falco falcosecurity/falco \
--namespace falco-system --create-namespace \
--set falcosidekick.enabled=true \
--set falcosidekick.config.slack.webhookurl="https://hooks.slack.com/services/xxx"
# Run kube-bench for CIS Kubernetes Benchmark assessment
kubectl apply -f https://raw.githubusercontent.com/aquasecurity/kube-bench/main/job-eks.yaml
kubectl logs -l app=kube-bench
Key Concepts
| Term | Definition |
|---|---|
| Pod Security Standards | Three profiles (Privileged, Baseline, Restricted) enforced via Pod Security Admission that control pod security context capabilities |
| Workload Identity | Cloud-native mechanism binding Kubernetes service accounts to cloud IAM roles for credential-free cloud API access (IRSA, GKE WI, AKS MI) |
| Network Policy | Kubernetes resource defining allowed ingress and egress traffic flows between pods, enforced by the CNI plugin |
| Admission Controller | Kubernetes plugin that intercepts API requests before persistence to validate or mutate resources against security policies |
| RBAC | Role-Based Access Control in Kubernetes, defining what actions (verbs) identities can perform on which resources in which namespaces |
| Seccomp Profile | Linux kernel feature restricting the system calls a container process can make, reducing the kernel attack surface |
| Service Mesh | Infrastructure layer (Istio, Linkerd) providing mutual TLS, traffic policies, and observability for service-to-service communication |
Tools & Systems
- Falco: Open-source runtime security engine detecting anomalous behavior in containers using kernel-level system call monitoring
- Kyverno: Kubernetes-native policy engine for admission control, mutation, and generation of resources based on security policies
- kube-bench: CIS Kubernetes Benchmark assessment tool checking cluster configuration against security best practices
- Trivy: Vulnerability scanner for container images, file systems, and Kubernetes resources with SBOM generation
- Calico/Cilium: CNI plugins providing network policy enforcement and advanced network security features including eBPF-based monitoring
Common Scenarios
Scenario: Cryptominer Deployed via Compromised Container Image
Context: GuardDuty Extended Threat Detection generates an AttackSequence:EKS/CompromisedCluster finding. A developer pulled a public Docker image containing an embedded XMRig cryptominer that executes at container startup.
Approach:
- Isolate the affected pod by applying a deny-all network policy targeting its labels
- Capture the container image digest and scan it with Trivy to identify the embedded binary
- Review Kubernetes audit logs to identify who deployed the compromised image and when
- Deploy Kyverno ClusterPolicy requiring images from approved private registries only
- Enable image digest pinning to prevent tag mutation attacks
- Deploy Falco with rules detecting crypto mining process signatures (/usr/bin/xmrig, stratum+tcp connections)
Pitfalls: Deleting the pod before capturing the image digest and audit logs destroys forensic evidence. Blocking only the specific image tag allows the attacker to re-push with a different tag.
Output Format
Kubernetes Security Assessment Report
=======================================
Cluster: production-cluster (EKS 1.29)
Provider: AWS (us-east-1)
Assessment Date: 2025-02-23
Tool: kube-bench v0.8.0 + manual review
CIS KUBERNETES BENCHMARK RESULTS:
Total Controls: 124
Passed: 98 (79%)
Failed: 18 (15%)
Warnings: 8 (6%)
CRITICAL FINDINGS:
[K8S-001] 3 namespaces lack Pod Security Standards enforcement
Namespaces: monitoring, logging, default
Remediation: Apply restricted PSA labels
[K8S-002] Default service account tokens auto-mounted in 12 deployments
Risk: Credential theft if container is compromised
Remediation: Set automountServiceAccountToken: false
[K8S-003] No network policies in production namespace
Risk: Unrestricted lateral movement between all pods
Remediation: Deploy default-deny policy with explicit allow rules
HIGH FINDINGS:
[K8S-004] 5 pods running as root with privileged security context
[K8S-005] Images deployed using mutable tags (:latest) in 8 deployments
[K8S-006] RBAC ClusterRoleBinding grants cluster-admin to developers group
How to use securing-kubernetes-on-cloud on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add securing-kubernetes-on-cloud
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches securing-kubernetes-on-cloud from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate securing-kubernetes-on-cloud. Access the skill through slash commands (e.g., /securing-kubernetes-on-cloud) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★47 reviews- ★★★★★Pratham Ware· Dec 28, 2024
Solid pick for teams standardizing on skills: securing-kubernetes-on-cloud is focused, and the summary matches what you get after install.
- ★★★★★Luis Mehta· Dec 24, 2024
Solid pick for teams standardizing on skills: securing-kubernetes-on-cloud is focused, and the summary matches what you get after install.
- ★★★★★Isabella Ndlovu· Dec 24, 2024
I recommend securing-kubernetes-on-cloud for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Michael Wang· Dec 12, 2024
securing-kubernetes-on-cloud fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yash Thakker· Nov 19, 2024
We added securing-kubernetes-on-cloud from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Camila Khan· Nov 15, 2024
We added securing-kubernetes-on-cloud from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kaira Jackson· Nov 15, 2024
securing-kubernetes-on-cloud reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Michael Park· Nov 3, 2024
securing-kubernetes-on-cloud has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kofi Martin· Oct 22, 2024
Solid pick for teams standardizing on skills: securing-kubernetes-on-cloud is focused, and the summary matches what you get after install.
- ★★★★★Dhruvi Jain· Oct 10, 2024
securing-kubernetes-on-cloud fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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