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.
University of Kalyani（卡利亚尼大学）；National Institute of Science, Education and Research Bhubaneswar（布巴内斯瓦尔国家科学教育与研究学院）；Homi Bhaba National Institute（霍米·巴巴国家学院）
Pushed by Accident: A Mixed-Methods Study on Strategies of Handling Secret Information in Source Code Repositories
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Leibniz University Hannover（汉诺威莱布尼茨大学）；Paderborn University（帕德博恩大学）；The George Washington University（乔治华盛顿大学）
Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；Information Engineering University（信息工程大学）；Henan Key Laboratory of Information Security（河南省信息安全重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
