Data Loss Prevention (DLP) has become crucial in the era of Generative AI (GenAI), where traditional methods struggle to address new risks associated with AI-driven environments. As organizations increasingly use GenAI tools, the risk of unintentional data leakage grows, requiring modern DLP solutions that go beyond mere compliance to become strategic necessities. These solutions must understand language and context, support workflows involving large language models (LLMs), and provide real-time visibility into data movement. Unlike traditional DLP systems that rely on static data patterns, next-gen DLP tools need to operate at a semantic level, understanding the meaning behind data and preventing leaks through language-based transformations such as summarization, paraphrasing, and translation. The shift to GenAI requires DLP to adapt by offering sensitive data classification, customizable policy enforcement, and real-time monitoring, ultimately redefining data protection to meet the complex demands of today's AI-driven data landscapes.