Managing Information Security Risks of Electronic Document Management Systems Based on Neural Network Models

The article examines the use of artificial neural networks for managing information security risks in electronic document management systems. The paper considers the transition from traditional protection tools based mainly on signature rules and expert procedures to adaptive models for security event analysis. Particular attention is paid to the specifics of electronic document management as a digital environment that simultaneously processes legally significant documents, personal data, business correspondence and technological logs. The study shows that the rapid growth of electronic document flows, the distributed nature of organizational processes and the increasing complexity of network infrastructure require a revision of approaches to monitoring and prioritizing threats. As a promising solution, the article proposes a neural network-based risk-oriented security framework that includes a sensor layer for event collection, an intelligent TrafficLLM correlation module and a continuous retraining mechanism based on EA-PEFT parameter-efficient adaptation. This architecture makes it possible to take data drift into account, detect non-standard patterns of user and network behavior, and generate risk assessments without significantly increasing the operational workload. The scientific novelty of the study lies in the development of an integrated model for applying neural network technologies to EDMS information security risk management: from the classification of organizational, administrative, subjective and technological risks to the description of model integration into electronic document management infrastructure. The practical significance consists in the possibility of applying the proposed approach in organizations with distributed structures, intensive circulation of legally significant electronic documents and strict requirements for business process continuity.

1. Varfolomeeva V.A., Ivanova N.A. Electronic document management, its advantages, disadvantages, risks. Journal of Applied Research. 2022;6-3:192-197. (In Russ.). URL: https://doi.org/10.47576/2712-7516_2022_6_3_192

2. Babash A.V., Baranova E.K. Actual issues of information protection: A monograph. Moscow: RIOR: INFRA-M; 2017. URL: https://www.elibrary.ru/ykqffc (In Russ.).

3. Kovalev E.A. Application of artificial neural networks in information security systems. Safety. Management. Artifi Intelligence. 2022;4(4(4)):26-35. URL: https://www.elibrary.ru/thnloh (In Russ.).

4. Mikryukov A.A., Babash A.V., Sizov V.A. Classification of events in information security systems based on neural network technologies. Open Education. 2019;23(1):57-63. (In Russ.).URL: https://doi.org/10.21686/1818-4243-20191-57-63

5. Plugatarev A.V., Marukhlenko A.L., Bugorskiy M.A., Bulgakov A.S., Marchenko M.A. Application of neural networks in information security systems. Information Technology Security. 2021;28(3)73-80. (In Russ.).URL: https://doi.org/10.26583/bit.2021.3.06

6. Bolshakov A. S., Khusainov R.V., Osin A.V. Detection of traffic anomalies using a neural network to ensure information security. I-Methods. 2021;13:4. (In Russ.). URL: https://www.elibrary.ru/pkcxwm

7. Khadzhieva L.K., Chadaev A.K. Cybersecurity and artificial intelligence: the use of artificial intelligence to detect and prevent cyber attacks. Economics and Management: Problems, Solutions. 2025;2-12(165):97-103. (In Russ.). URL: https://doi.org/10.36871/ek.up.p.r.2025.12.02.011

8. Baranova E.K., Kryuchkov E.S. Neural network approach to minimizing information security risks of electronic document management systems. In: Trends in the development of the Internet and the digital economy. Simferopol; 2025;217-221. URL: https://www.elibrary.ru/fdzhfk (In Russ.).

9. Saenko I.B., Kotenko I.V., Al-Barry M.H. The use of artificial neural networks to detect abnormal behavior of users of data centers. Cybersecurity Issues. 2022;2(48):87-97. (In Russ.). URL: https://doi.org/10.21681/2311-3456-20222-87-97

10. Simavorian S.Zh., Simonyan A.R., Popov G.A., Ulitina E.I. The general concept of detecting intrusions of an unknown type based on neural networks. Software Systems and Computational Methods. 2021;4:23-45. (In Russ.). URL: https://doi.org/10.7256/2454-0714.2021.4.37072

11. Reznik D.V. Artifi neural networks. Analysis of the possibilities of using it to ensure information security. The Scientifi Heritage. 2021;67(1(67)):50-53. (In Russ.). URL: https://doi.org/10.24412/9215-0365-2021-67-1-50-53

12. Osipova V.P. Application of neural networks in the fi of banking system security. In: Fundamental and Applied Research in Economics and Finance. Orel; 2021;131-133. URL: https://www.elibrary.ru/mxffbf (In Russ.).

13. Budzko V.I., Belenkov V.G., Korolev V.I., Melnikov D.A. Information security features of automated systems using neural network technologies. High Availability Systems. 2023;19(3):5-17. URL: https://www.elibrary.ru/qrerqd (In Russ.).

14. Zhernova K.N. Review of the use of neural networks in information security. Informatization and Communication. 2024;4:109-122. (In Russ.).URL: https://doi.org/10.34219/2078-8320-2024-15-109-122

15. Frolov P.V., Chukhraev I.V., Grishanov K.M. Application of artificial neural networks in intrusion detection systems. System Administrator. 2018;9(190):80-83. URL: https://www.elibrary.ru/wlyfoj (In Russ.).