This paper introduces a system designed to automatically identify threads related to data breaches in underground forums. The system operates in real-time, monitoring both surface and dark web sources. It employs feature extraction using the LDA topic model to analyze thread content. By comparing various supervised classification algorithms, the study identifies the most effective method, achieving over 92% accuracy in detecting data breach threads in the experimental dataset.