Data governance and security have emerged as major challenges for C-suite leaders pursuing AI, with 44% of executives citing these issues as major obstacles in their AI efforts. Robust security and governance frameworks are crucial to manage data across interconnected systems, particularly in regulated sectors like government and financial services. Centralizing operational data into data lakes or warehouses can pose non-trivial risks in setting appropriate data access controls for downstream AI use cases. Ensuring the right tools and controls are in place to monitor and manage both data and its usage is a new challenge for most enterprises. Data used to train models requires additional security measures to protect it, particularly when using third-party AI services. Failing to manage these risks can result in security breaches, financial loss, and damage to an organization's reputation. Implementing robust data governance frameworks is essential to mitigate these risks, including setting clear policies for data usage, access, and storage, and enforcing them consistently across all AI initiatives.