Integrated method for assessing expert competence (IMAEC) in state information security

The relevance of this study is grounded in the need to improve methods for evaluating expert competence in the field of state information security. The increasing number of threats in cyberspace and the growing demands on specialist qualifications necessitate innovative decision-making approaches that can account for subjective factors and data uncertainty. This research aimed to develop and test an integrated approach combining the Analytic Hierarchy Process (AHP) with fuzzy logic for the assessment of candidates for expert positions in information security. The AHP enabled the problem to be structured as a hierarchical model encompassing the goal, criteria (experience, certifications, communication skills), and alternatives (candidates). The AHP methodology involved pairwise comparison of criteria, calculation of weights, and consistency checks of the matrices. Fuzzy logic complemented AHP by enabling the processing of imprecise data through fuzzification, the application of “If-Then" rules, and defuzzification. Practical validation of the method was carried out using the example of candidate evaluation based on the specified criteria. The research findings demonstrated that the proposed approach enabled the integration of both precise and imprecise elements of assessment, thereby improving the accuracy and justification of decisions. The Monte Carlo method was employed to verify the model’s reliability, ensuring result stability through repeated simulations of data variations. The results confirmed the method’s high adaptability to handling uncertain data. The practical value of the study lies in the application of the integrated approach to enhance the efficiency of personnel selection – an essential component in safeguarding information security within state institutions. The proposed method may also be applied to other multi-criteria decision-making problems under conditions of uncertainty