In the rapidly evolving world of AI, Retrieval-Augmented Generation (RAG) is gaining traction. RAG allows Large Language Models (LLMs) to access and use external data, making them smarter and more versatile. But this raises a critical question: How do we protect data ownership when LLMs can tap into vast amounts of information? Imagine a scenario where someone uses your proprietary data to train their RAG system without permission. Proving this unauthorized use has been a significant challenge—until now. Researchers have developed a novel technique called "WARD," which uses LLM watermarks to detect unauthorized data usage in RAG systems. Think of it as a digital fingerprint. Data owners can embed these watermarks into their documents before releasing them publicly. Then, by querying the RAG system, they can detect traces of these watermarks in the generated responses. Even small traces are enough to raise a red flag. The beauty of this approach lies in its statistical rigor. WARD offers strong guarantees about the likelihood of false accusations, ensuring that RAG providers are not unfairly targeted. Furthermore, WARD works even if the RAG provider tries to hide the data usage. It’s like trying to erase a digital watermark – it’s incredibly difficult. This breakthrough has significant implications for data ownership in the age of AI. It offers data owners a powerful tool to audit RAG systems and protect their intellectual property. This innovation could reshape how we think about data security, setting a precedent for safeguarding information in an increasingly AI-driven world. While the current research focuses on text-based data, the implications extend to other data formats, potentially revolutionizing data protection across various industries. As AI continues to advance, protecting data ownership will become even more critical. WARD is a crucial step toward a future where AI benefits everyone while respecting the rights of data owners.