Ai-Enhanced Data Loss Prevention Systems: A Machine Learning Approach to Intelligent Data Security

The exponential proliferation of digital data within organizations has raised the possibility of data breaches and illicit data transfers. Traditional Data Loss Prevention (DLP) solutions, which use static rule-based procedures, frequently fail to detect complex cyber threats and insider misuse. This study describes an AI-driven DLP system that uses machine learning techniques to improve detection accuracy, reduce false positives, and enable adaptive policy enforcement. A synthetic dataset reflecting user activity and file access patterns was used to train algorithms such as Random Forest and K-Means clustering to detect abnormal activities. The experimental results show a significant improvement in detection accuracy (92%), as well as response time, when compared to conventional methods. The study finds that the incorporation of AI into DLP offers a potential option for current.