Enterprises are racing to deploy Generative AI, rapidly spinning up infrastructure across AWS, Azure, and Google Cloud. In this fragmented, hyper-accelerated reality, an AI application might ingest data from an AWS S3 data lake, train on GCP Gemini Enterprise Agent Platform (formerly Vertex AI), and serve inference through Azure OpenAI. Traditional cloud security guardrails are often bypassed, creating massive blind spots. Adversaries aren't just targeting the language models; they are targeting the underlying multi-cloud infrastructure and exploiting the seams between providers.

This technical deep dive explores the unique attack surface of cloud-hosted AI workloads. We will deconstruct how threat actors exploit overly permissive ML training roles, manipulate unstructured data lakes for data poisoning, and abuse cloud metadata services to pivot laterally. Attendees will leave with a concrete architectural blueprint for translating network isolation, identity guardrails, and automated policies across the "Big Three" cloud providers to protect their AI infrastructure before the sprawl becomes unmanageable.