Securing AI at Enterprise Scale: The Google Kubernetes Engine Blueprint
Artificial intelligence is moving from prototype to production faster than traditional security paradigms can adapt. For CISOs and platform engineering teams, the challenge is clear: you need to protect proprietary model weights, defend against novel application-layer threats like prompt injection, and enforce strict regulatory compliance—all without slowing down your AI developers.
To meet all of these security goals, you need more than just a place to run containers; you need a platform that compounds layers of security out-of-the-box.
Today, we're sharing our blueprint for Best practices for AI workload security on Google Kubernetes Engine (GKE). This blueprint consolidates controls across multiple Google Cloud services and GKE features to help you to build a secure-by-default GKE platform that handles the realities of AI at scale.
The AI workload security blueprint for GKE identifies three critical layers of the AI stack. Here's how Google Cloud and GKE approach security at each...
Copyright of this story solely belongs to google.com. To see the full text click HERE
