Development of a multifactorial econometric model for assessing a country’s cybervulnerability in a context of geopolitical turbulence
DOI:
https://doi.org/10.15587/1729-4061.2026.363063Keywords:
national cybersecurity, panel logistic regression, geopolitical turbulence, cyber vulnerability, early warning systemsAbstract
The subject of the study was the national cyber vulnerability of sovereign states in a context of geopolitical turbulence. The study addresses the lack of proven, multi-factor econometric tools that would enable an accurate quantitative assessment of the impact of macroeconomic instability and military conflicts on the overall level of institutional cybersecurity. The results of the study showed that armed conflicts create a strong non-linear link with breaches of digital systems’ information security, leading to a 10.2 percentage point increase in critical system vulnerabilities during periods of war. This was explained by the combined effect of the ‘war multiplier’ and the ‘paradox of institutional inertia’, as demonstrated by official Computer Security Incident Response Teams (CSIRTs) which were unable to protect systems due to a lack of adequate funding. The study showed that gross domestic product (GDP) acts as a protective factor, as every 1% increase in GDP leads to a 10.1% reduction in risk. The study’s results were unique, as a pooled logit regression model with cluster-consistent standard errors was used to analyze panel data from 14 countries (n = 68), and the threshold values for cyberattacks were determined at the 75th percentile. The developed model was used to quantify the effect of detection bias during crisis situations. The mathematical framework of the developed model acted as a central element, enabling macroeconomic early warning systems to fully realize their potential in the allocation of defense resources and the protection of digital sovereignty
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Copyright (c) 2026 Oleksandr Kushnerov, Inna Tiutiunyk, Serhii Yevseiev, Ivan Opirskyy, Vladyslav Sokol, Olena Voloshchuk, Oleksandr Novoseletskyi, Yevhen Melenti, Iryna Husarova, Dmytro Balagura
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