Implementation of Security Systems for Detection and Prevention of Data Loss/Leakage at Organization via Traffic Inspection
This paper aims to improve data loss prevention for organizations by focusing on data in motion, offering an incremental solution to a known security challenge.
This paper addresses the problem of Data Loss/Leakage Prevention (DLP) for data in motion within organizations. It proposes a model solution combining existing methodologies with a new machine learning-based pattern matching content checker to protect data and take actions, along with a DLP deployment design at the gateway level using a squid proxy server and ICAP server.
Data Loss/Leakage Prevention (DLP) continues to be the main issue for many large organizations. There are multiple numbers of emerging security attach scenarios and a limitless number of overcoming solutions. Today's enterprises' major concern is to protect confidential information because a leakage that compromises confidential data means that sensitive information is in competitors' hands. Different data types need to be protected. However, our research is focused only on data in motion (DIM) i-e data transferred through the network. The research and scenarios in this paper demonstrate a recent survey on information and data leakage incidents, which reveals its importance and also proposed a model solution that will offer the combination of previous methodologies with a new way of pattern matching by advanced content checker based on the use of machine learning to protect data within an organization and then take actions accordingly. This paper also proposed a DLP deployment design on the gateway level that shows how data is moving through intermediate channels before reaching the final destination using the squid proxy server and ICAP server.