AI governance refers to the framework of policies, processes, and controls that guide the responsible use of AI technologies. It ensures AI operates within acceptable boundaries, ethically, legally, and strategically. However, AI governance adds layers of complexity compared to traditional data governance, including model behavior and interpretability, prompt input and response output control, bias mitigation in generated content, versioning and change management of semantic and model logic, auditability of AI-driven decisions, security and compliance for data used in AI training or querying. The scope is broader and the consequences of failure more public than traditional data governance. AI systems need to interpret and apply governance consistently in real time, which can be a challenge for organizations. A universal semantic layer like Cube Cloud's is essential to enable AI governance by centralizing business logic, ensuring consistency, and enabling AI to generate responses that are trusted, auditable, and aligned with enterprise standards.