2026 1(50) 27

Artificial intelligence as a tool for forming a proactive economic security system

Tkachenko A.,
Doctor of Economics Sciences, Professor,
ORCID https://orcid.org/0000-0002-1843-2579
e-mail: alla0676128584@gmail.com;
Pozhuieva T.,
Doctor of Economics Sciences, Professor,
ORCID https://orcid.org/0000-0002-9895-2557
e-mail: lowleyhome@gmail.com
National University “Zaporizhzhya Polytechnics”, Zaporizhzhya

Citation Format
Tkachenko, A., & Pozhuieva, T. (2026). Artificial intelligence as a tool for forming a proactive economic security system. Vіsnyk ekonomіchnoі nauky Ukraіny, 1(50), 236-242. https://doi.org/10.37405/3041-1629.2026.1(50).236-242

Language
 Ukrainian

Resume
The article explores the theoretical and methodological foundations for integrating artificial intelligence into the economic security management system of an enterprise in the context of accelerating digital transformation. The research substantiates the necessity of shifting from a traditional reactive security model, focused on mitigating already realized threats, to a proactive framework based on predictive analytics, early risk detection, and anticipatory managerial decision-making. The study argues that contemporary economic turbulence, growing complexity of business environments, and the expansion of digital ecosystems require fundamentally new approaches to ensuring enterprise resilience.
Building upon recent advances in economic security theory and AI-driven risk management, a comprehensive conceptual architecture of a proactive economic security system is proposed. The model integrates five interrelated components: an information-analytical block, an algorithmic block based on machine learning models, a risk indicator subsystem, a managerial decision-making block, and an AI governance framework. Special attention is devoted to the development of an integrated Proactive Security Index designed to quantitatively assess the effectiveness of AI implementation within enterprise risk management processes.
The empirical modeling results demonstrate that the application of gradient boosting algorithms, neural networks, and anomaly detection techniques significantly improves the accuracy of financial and operational risk forecasting while reducing response time to potential threats. The findings confirm that algorithmic decision-support systems enhance predictive capabilities and contribute to strengthening financial stability and operational continuity.
The study also emphasizes the critical role of AI governance mechanisms in ensuring transparency, reliability, accountability, and compliance of algorithmic systems. The proposed framework offers a structured methodological basis for designing adaptive early-warning risk systems and can be applied across different sectors of the economy. Overall, the research contributes to the advancement of proactive economic security management by integrating technological, analytical, and strategic dimensions into a unified enterprise architecture.

Keywords
economic security of enterprise; proactive management system; artificial intelligence; predictive analytics; risk management; machine learning; AI governance.

Referensces

  1. Yermolenko, O. A., & Nasrullayev, R. S. (2025). Principles of Ensuring the Economic Security System of Enterprises in the Context of Digitalization. The Problems of Economy, 1(63), 158–164. https://doi.org/10.32983/2222-0712-2025-1-158-164 [in Ukrainian].
  2. Piletska, S. T., Arefiev, S. O., Petrovska, S. V., & Kolesnykov, S. O. (2024). Strategic Ensuring of Economic Security of Enterprises in the Context of Digitalization of the Economy of Ukraine. The Problems of Economy, 2(60), 181–190. https://doi.org/10.32983/2222-0712-2024-2-181-190 [in Ukrainian].
  3. Tkachuk, H. O., Ivanchenkova, L. V., & Zghadova, N. S. (2025). The Role of Digital Technologies in Information and Analytical Support of Economic Security. The Problems of Economy, 1(63), 100–106. https://doi.org/10.32983/2222-0712-2025-1-100-106 [in Ukrainian].
  4. Khalina, O., & Shmahalo, V. (2025). Strategy for the Development of Economic Security of Enterprises in the Context of Digital. Economy and Society, 73. https://doi.org/10.32782/2524-0072/2025-73-138 [in Ukrainian].
  5. Oriekhov, D. (2024). Usage of Artificial Intelligence in Management of Modern Enterprise. Economy and Society, 64. https://doi.org/10.32782/2524-0072/2024-64-143 [in Ukrainian].
  6. Liang, X., He., Q., & Jin, T. (2025). Chain-leading enterprises’ artificial intelligence adoption and supply chain disruption risk. Economics Letters, 254, 112502. https://doi.org/10.1016/j.econlet.2025.112502
  7. Tian, K., Zhu, Z., Mbachu, J., Ghanbaripour, A., & Moorhead, M. (2025). Artificial intelligence in risk management: A bibliometric analysis and systematic review. Journal of Innovation & Knowledge, 10(3), 100711. https://doi.org/10.1016/j.jik.2025.100711
  8. Chen, W. (2025). Enterprise financial risk prediction and intelligent early warning model based on deep learning. Discover Artificial Intelligence, 5, 227. https://doi.org/10.1007/s44163-025-00497-1
  9. National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1 and U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1
  10. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams M. D. (2023). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
  11. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
  12. Batarseh, F. A., & Freeman, L. (2022). AI assurance: Towards trustworthy, explainable, safe, and ethical AI. Academic Press. https://doi.org/10.1016/C2021-0-00128-0
  13. Novikova, N., Diachenko, O., Tkachenko, A., Chorna, N., Chornyi, R., & Krylov, M. (2025). The Application of Artificial Intelligence in Facilitating Analytical Support for the Operations of Governmental Institutions. Sustainable Data Management, Studies in Big Data, 171, 171–182. https://doi.org/10.1007/978-3-031-83911-5_15
  14. Ransbotham, S., Candelon, F., Kiron, D., LaFountain, B., & Khodabandeh, S. (2021, November). The cultural benefits of artificial intelligence in the enterprise. MIT Sloan Management Review and Boston Consulting Group. https://web-assets.bcg.com/85/90/95939185404cbd901aba0d54f1d7/the-cultural-benefits-of-artificial-intelligence-in-the-enterprise-r.pdf

Full Text (.pdf)

Received: 02.04.2025
Accepted: 08.05.2026
Published: 29.05.2026