In today's digital environment, organizations often fail to detect in real-time when their data is leaked and sold online. Our goal is to shorten the time gap between the exposure of data on the internet and its detection by the public, thereby minimizing the duration in which sensitive corporate data remains exposed. The dark web serves as a primary marketplace for trading personal information and can be accessed securely through browsers like Tor browser. This paper focuses on web crawling of dark web sites. Utilizing data collected from these sites, we trained a BERT classification model to categorize transaction posts into five different types of data breaches. This enables rapid identification of the type of leak each post pertains to. Finally, we employ a Retrieval-Augmented Generation (RAG) approach to gain insights from the dark web.