Month/Year of Graduation

5-2026

Degree Name

Bachelor of Science (B.S.)

Department

Computer Science

First Advisor

Harvey Siy

Abstract

Data Loss Prevention (DLP) systems play a critical role in protecting modern systems that handle sensitive information from both accidental and malicious exposure. Traditional DLP approaches often rely on static rules and methods that can struggle to adapt to complex and evolving data patterns. This paper presents a hybrid DPL system that integrates machine learning-based message classification, rule based policy enforcement, and context-aware access control to improve both detection accuracy and decision reliability. In addition, the system introduces a second stage access control model that evaluates user context, including role of clearance level and job title to determine whether access to a specific level of message should be allowed. Results from this system demonstrates high classification performance while highlighting the challenges of under-classification and the less suitable accuracy for message denial. Overall, this work illustrates how integrating machine learning with traditional security mechanisms can produce more adaptive and robust DLP systems capable of addressing real-world security challenges.

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Reviewed and passed for accessibility

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