Real-Time Email Security Through Proxy-Integrated DistilBERT DLP Framework

Data Loss Prevention (DLP) is a crucial security strategy that protects sensitive information from unauthorized access or leakage. Traditional DLP systems, which use static rules and keyword matching, are often ineffective against modern, complex threats and can produce many false positives [1]. To overcome these challenges, this project proposes an advanced Machine Learning (ML)-based DLP solution. Our approach leverages a fine-tuned DistilBERT model, a lightweight yet powerful version of BERT, for its speed, accuracy, and scalability in classifying emails. The proposed architecture integrates the AI-DLP Engine into a proxy server, enabling real-time analysis of email content, metadata, and behavioral patterns to classify data flows. Evaluation results demonstrate the system's ability to identify highly sensitive information with very high confidence, making it a dynamic and proactive approach to enterprise data security [2].