Data Leakage System

Abstract

Data leakage detection is a critical aspect of information security, aiming to identify and prevent unauthorized or unintentional transmission of sensitive data outside the intended boundaries of an organization. The abstract for a data leakage detection system typically provides a concise overview of the key components, objectives, and methodologies involved in safeguarding sensitive information. With the increasing reliance on digital platforms and the interconnected nature of modern organizations, the risk of data leakage has become a pressing concern. This research proposes a robust Data Leakage Detection System (DLDS) designed to identify and prevent unauthorized data disclosure. The system employs a multi-faceted approach, integrating anomaly detection, signature-based detection, and content inspection techniques to comprehensively analyze data flows within and outside organizational boundaries. The DLDS utilizes advanced machine learning algorithms to profile normal data behavior and detect deviations that may indicate potential leaks. Signature-based detection employs predefined patterns to identify known data leakage patterns and signatures, enhancing the system's ability to recognize well-established threat vectors. Content inspection is implemented to examine the actual content of data packets, ensuring a granular analysis of sensitive information.