Abstract Generative Artificial Intelligence (GenAI) has evolved into a transformative technology whose unprecedented growth and public exposure have revealed challenging issues ranging from privacy protection to reducing factual inaccuracies and hallucinations, model security risks, legal complications, and a lack of interpretability. This position paper examines how Differential Privacy (DP), a mathematical privacy protection framework, can address both privacy concerns and other systemic chall