Securing the AI Lifecycle - Trust, Transparency and Tooling in Open Source

Explore how to build security into AI/ML pipelines from the ground up in this 51-minute tech talk featuring leading practitioners from the open source community. Learn to strengthen transparency and reproducibility using open standards while discovering essential tools for model signing, metadata management, and secure development practices. The session spotlights the new industry resource "Visualizing Secure MLOps (MLSecOps): A Practical Guide for Building Robust AI/ML Pipeline Security," which provides a visual, open source-centric approach to securing the AI lifecycle. Gain insights into how open source technologies drive the creation of trusted, transparent, and resilient AI systems, making this valuable content for developers, researchers, and policymakers working with artificial intelligence and machine learning technologies.