Securing AI Pipelines - Real-World Attacks on Kubernetes-Based AI Infrastructure

Explore real-world security vulnerabilities in Kubernetes-based AI infrastructure through live demonstrations of attacks targeting machine learning deployments. Learn how attackers can exploit AI model serving endpoints to steal proprietary models worth millions and compromise underlying infrastructure, starting from seemingly innocent deployment patterns like Stable Diffusion models. Discover the unique attack surfaces created when ML engineers deploy AI workloads to Kubernetes clusters and understand why traditional security approaches fall short. Gain practical defensive strategies developed from enterprise AI deployment experiences, including implementing custom admission controllers, establishing GPU isolation patterns, and deploying monitoring strategies specifically designed for AI workloads. Master the security challenges that arise when AI meets container orchestration and develop concrete measures to protect your AI pipeline infrastructure from sophisticated attacks.