Deep Learning-Based Data Leak Prevention Systems for Enterprise Environments

Greesham Anand; Shivaraj Yanamandram Kuppuraju; Sambhav Patil

doi:10.36948/ijfmr.2025.v07i02.43098

Deep Learning-Based Data Leak Prevention Systems for Enterprise Environments

Author(s)	Mr. Greesham Anand, Mr. Shivaraj Yanamandram Kuppuraju, Mr. Sambhav Patil
Country	India
Abstract	This paper presents a novel approach to enhancing Data Leak Prevention (DLP) systems within enterprise environments through the application of deep learning techniques. Traditional DLP methods often struggle to effectively detect complex data exfiltration patterns and adapt to evolving threats, particularly when dealing with unstructured data formats and insider risks. To address these challenges, the proposed system leverages advanced deep learning models, including Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, to analyze structured and unstructured enterprise data in real-time. The system demonstrates high performance across key evaluation metrics, achieving an accuracy of 94.7%, precision of 93.1%, recall of 91.5%, F1-score of 92.3%, and AUC-ROC of 96.1%, significantly outperforming traditional DLP approaches. The integration of natural language processing and behavioral analytics enhances the system’s ability to detect sensitive data leaks with greater context awareness and minimal false positives. Additionally, the methodology incorporates real-world enterprise datasets and adheres to regulatory compliance standards, ensuring practical applicability and legal alignment. The findings underscore the potential of deep learning to transform enterprise data protection strategies by providing scalable, adaptive, and intelligent DLP solutions.
Keywords	Deep Learning, Data Leak Prevention, Enterprise Security, Natural Language Processing, Insider Threat Detection.
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-27
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.43098

View / Download PDF File

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Deep Learning-Based Data Leak Prevention Systems for Enterprise Environments

Share this