As AI systems proliferate, ensuring transparency, trust, and traceability across the model supply chain has become a critical challenge. The Model Openness Framework (MOF), developed by LF AI & Data and Generative AI Commons, offers a standardized classification system to evaluate the completeness and openness of AI models across 17 key components—from architecture to evaluation code and documentation. This talk will explore how the MOF addresses model "openwashing" and supply chain risk by establishing clear standards for licensing and disclosure. We will demonstrate how enterprises can operationalize MOF compliance using open source tools like OCI-based model packaging, model signing, and automated documentation pipelines. Attendees will gain practical insights into aligning with emerging governance requirements and building trustworthy, reproducible AI systems through open collaboration.