APPLICATION OF OPTIMAL DECISION-MAKING MODELS IN ASSESSMENT AND MANAGEMENT OF CYBERSECURITY RISKS

Abstract

This study investigates how cybersecurity risks, which have intensified with the acceleration of digitalization, can be assessed more effectively. The research is not limited to a theoretical framework; it also demonstrates the application of the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on a practical scenario developed for the financial sector. The findings indicate that the integration of these approaches yields more effective outcomes in terms of risk reduction and optimal resource allocation compared to traditional methods. The study further examines cybersecurity risks from technical, organizational-managerial, and human-factor perspectives, while presenting the decision-making process in a clear and structured manner through both qualitative and quantitative approaches. In addition, the applicability of methods such as fuzzy logic, Bayesian approaches, and Monte Carlo simulation in this domain is discussed. Moreover, the research proposes a conceptual model that integrates classical decision-making models with artificial intelligence-based systems. This framework enables the alignment of strategic-level decisions with real-time operational processes executed through SOAR platforms. Furthermore, incorporating economic indicators such as ROI and ALE facilitates a closer alignment between cybersecurity measures and broader business objectives.