GKE Blueprint for Securing AI Workloads at Scale
🔒 This article presents a blueprint for securing AI workloads on Google Kubernetes Engine (GKE), consolidating controls across Google Cloud services and GKE features to create a secure-by-default platform. It covers three layers—infrastructure, supply chain, and application—and details capabilities such as Confidential GKE Nodes, Workload Identity Federation, k8s-aibom for AI SBOMs, Model Armor, and the GKE Inference Gateway. The blueprint recommends a three-phase rollout: Deploy, Operate, and Govern, and emphasizes integrating Google Cloud controls to maintain security at enterprise scale.
