Trusted AI at Scale: Secure Governance and Scalable Management for Your AI Models

As AI becomes an indispensable part of modern software applications, managing machine learning models with the same rigor as code and binaries is essential. Yet most organizations still treat models as ad-hoc assets: scattered, untracked, and inconsistently governed, creating potentially serious risks around security, compliance, and operational trust. Reminding us of yesterday’s OSS package gold rush, today’s ML/AI Models can originate from many sources: custom-built, open-source, and third-party APIs, each with different risks, ownership boundaries, and lifecycle considerations. In this session, we’ll explore these emerging challenges, and show how advancements in JFrog ML and platform technologies are helping solve them. By treating every type of model as a first-class software artifact, you’ll learn how to integrate model management into your existing DevSecOps pipeline, enable trust by providing visibility, traceability, and evidence-based policy enforcement, and bring the same governance and trust to AI that you already rely on for your software supply chain. It’s time to take back control of AI!