AI and ML are rapidly becoming foundational technologies for enterprises, offering powerful enhancements to products and services. However, onboarding and securing MLops and LLMOps tools while enabling access to real-world data is particularly challenging, especially in highly regulated industries like healthcare.

This session will provide a practical, security-focused deep dive into reference architectures for securing ML and LLM workloads across multi-cloud environments. We will explore native security controls in AWS and Azure, access patterns for sensitive data, and best practices for protecting AI workloads from unauthorized models and insecure data pipelines.

Attendees will gain actionable insights into designing secure AI/ML environments while balancing performance and compliance needs, including real-world lessons learned from platform architects navigating this evolving landscape.