AI governance is a comprehensive framework that ensures the responsible, ethical, and effective use of artificial intelligence by integrating data governance principles with model oversight. It is built on key pillars, including trustworthiness (ensuring data is complete, accurate, and relevant), transparency (making AI decisions explainable and accountable), ethics (ensuring fairness and preventing bias), security (protecting data through access controls and versioning), safety (mitigating risks like hallucinations, jailbreaks, and data leakage), and performance (monitoring data and model health through observability tools). Additionally, AI models themselves must be versioned, documented, and continuously monitored for drift to ensure they remain reliable and aligned with their intended purpose. As organizations mature in their data governance efforts, AI governance introduces new complexities, requiring enterprises to address emerging challenges such as bias prevention, unintended consequences, and fairness. While many organizations have established traditional data governance practices, generative AI has heightened the urgency to refine governance models that not only regulate AI but also leverage AI to enhance data governance itself.