Open-source AI is shaping how we build, deploy, and scale systems and applications today, right into production. But with the rapid adoption of upstream AI models, datasets, and orchestration tools comes a critical question: can we trust what we’re using and how it was originally created? According to PwC survey, about 50% of surveyed company leaders in 2025 admitted they don’t trust AI to be embedded in their core operations.
Unlike focusing on securing AI systems themselves, in this talk we’ll explore often overlooked topic – how data provenance, model transparency, and AI-specific supply chain security are becoming essential for building trustworthy AI systems.
I’ll cover the importance of data provenance and how they reduce risks like data poisoning, bias, and adversarial manipulation; the rise of the AI Software Bill of Materials (AI SBOM) to document model components and inference behavior; open-source tools that bring it all to life: Sigstore, KitOps, Model and Data Cards.
I will also share updates from our work in top AI standardization organizations like OASIS, OpenSSF, and LF AI & Data to define and support AI provenance standards, automation, and trusted AI guidelines.