Development of a DLP Module to Protect Personal Data in a Distributed Automated System

The importance of personal data protection in automated systems is being considered. A study is provided on the development of a software module for protecting confidential information, including personal data, in a distributed automated system. The primary goal of the study: to create is to create software that prevents leaks of confidential information. The developed software module is designed to block the leakage of confidential information through network channels and removable media using a machine learning model with centralized security administrator management. The practical significance: the use of this module in organizations includes ensuring centralized information security management, reducing the risk of confidential data leaks, and supporting the investigation of security incidents. The research can be used as a temporary measure during the period of updating and adapting the system to new realities until a full-fledged protection system is achieved.

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Publication Details

Journal
Intellectual Technologies on Transport
Published
2024-10-09
DOI
https://doi.org/10.20295/2413-2527-2024-339-65-72
Primary Topic
Cloud Data Security Solutions
Type
article
Field-Weighted Citation Impact
0.00
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article

Development of a DLP Module to Protect Personal Data in a Distributed Automated System

Arkadiy Kovtunenko
1 links
Intellectual Technologies on Transport
Cloud Data Security Solutions
article

Development of a DLP Module to Protect Personal Data in a Distributed Automated System

Arkadiy Kovtunenko
article en

Abstract

The importance of personal data protection in automated systems is being considered. A study is provided on the development of a software module for protecting confidential information, including personal data, in a distributed automated system. The primary goal of the study: to create is to create software that prevents leaks of confidential information. The developed software module is designed to block the leakage of confidential information through network channels and removable media using a machine learning model with centralized security administrator management. The practical significance: the use of this module in organizations includes ensuring centralized information security management, reducing the risk of confidential data leaks, and supporting the investigation of security incidents. The research can be used as a temporary measure during the period of updating and adapting the system to new realities until a full-fledged protection system is achieved.

Intellectual Technologies on TransportVol. 0(3)
Petersburg State Transport University (RU)
Peace, Justice and strong institutions
Openalex Percentile: Top 72%
Cloud Data Security Solutions
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