Tool and policy library for validating Google Kubernetes Engine clusters against configuration best practices and scalability limits.
GKE Policy Automation is an open-source tool and policy library for validating Google Kubernetes Engine clusters against configuration best practices and scalability limits. It automates compliance checks to ensure clusters are secure, reliable, and optimized, helping teams proactively identify and address potential issues. The tool supports both best practices validation and scalability monitoring, integrating with various GCP services for data collection and result reporting.
DevOps engineers, SREs, and platform teams managing GKE clusters who need to enforce compliance, security, and operational best practices at scale. It is also suitable for organizations requiring automated, continuous validation of their Kubernetes infrastructure across multiple projects or environments.
Developers choose GKE Policy Automation for its comprehensive, Google-native policy library, flexibility in data inputs and outputs, and support for multi-cluster validation via discovery. Its ability to integrate custom policies and serverless execution enables tailored, automated compliance workflows, reducing manual oversight and improving cluster reliability.
Tool and policy library for reviewing Google Kubernetes Engine clusters against best practices
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Includes a built-in library of best practices and scalability limits validated by Google, ensuring alignment with GKE recommendations without needing to write policies from scratch.
Automatically discovers and validates clusters across projects, folders, or entire organizations using Cloud Asset Inventory, ideal for large, dynamic environments.
Supports outputting results to JSON files, Pub/Sub, Cloud Storage, or Security Command Center, making it easy to integrate into existing CI/CD or monitoring workflows.
Allows specifying custom Git repositories with RegO rules, enabling teams to enforce organization-specific compliance checks beyond the default policy library.
Designed for automated, scheduled executions via Cloud Scheduler and Cloud Run, enabling continuous compliance monitoring without manual intervention.
Exclusively works with GKE and relies on GCP APIs and services, making it unsuitable for hybrid, multi-cloud, or on-premise Kubernetes deployments.
Requires deployment and configuration of kube-state-metrics and Prometheus (or Google Cloud Managed Service for Prometheus) for scalability checks, adding operational overhead.
Creating custom policies necessitates learning RegO and the policy structure, which can be a barrier for teams without prior experience in policy-as-code.
Lacks native alerting mechanisms; teams must integrate outputs with external systems like Pub/Sub or monitoring tools for real-time notifications, requiring additional setup.