International Research Journal of Education and Technology

In the contemporary digital sphere, data preservation stands as a paramount concern for both commercial entities and individual users. The proliferation of digital menaces and internal incursions has exacerbated the vulnerabilities to data breaches, leading to substantial fiscal and reputational repercussions. A Data Leakage Prevention System (DLPS) is engineered to thwart unauthorized data disclosures by scrutinizing data movement, identifying anomalies, and tracing potential breaches. This investigation proposes a synergistic methodology that integrates machine learning, encoded watermarking, and behavioral analysis to bolster detection accuracy. It evaluates diverse methodologies, encompassing signature-centric and anomaly-centric detection, advanced learning techniques, and

distributed ledger technology integration, to ascertain their efficacy in mitigating data leaks. Furthermore, ethical considerations pertaining to data surveillance, adherence to international regulations, and prospective advancements in DLPS are explored. By leveraging artificial intelligence and real-time threat neutralization strategies, this research furnishes insights into scalable and automated solutions for safeguarding confidential information.

Keywords: Data Preservation, Digital Menaces, Machine Learning, Distributed Ledger Technology, Anomaly Detection, Encoded Watermarking