Data security and data quality are significant challenges facing federal agencies as they apply AI, ML, and data analytics to achieve their mission. These large agencies all have multiple data-driven initiatives being implemented or considered across functional domains. They range from personnel operations, acquisition efforts, and agency-specific missions for both national security to humanitarian aid. To scale these efforts and ensure successful outcomes, they need to employ a framework that addresses people, processes, and technology. In this session, the presenters discuss the core elements of a successful data security and quality framework, including best practices, potential pitfalls, and recommendations based on successes with a large federal agency.