ADAPTIVE CONTEXTUAL ACCESS CONTROL MODEL FOR ENHANCING THE RESILIENCE OF CRITICAL INFORMATION SYSTEMS

This paper proposes an adaptive access control model for critical information systems that integrates three groups of attributes: device posture, user role, and access environment parameters. Based on the analysis of contemporary Zero Trust approaches, the necessity of multifactor risk assessment for each access request is substantiated. The developed model forms an integral risk score using weighted coefficients and contextual parameters, enabling dynamic decision-making in the form of access permission, restricted access, additional authentication, or access denial. The proposed approach improves the accuracy of identifying risky access scenarios and contributes to strengthening the resilience of critical information systems. Future research directions include extending the model with behavioral attributes, utilizing statistical incident data, applying machine learning methods for adaptive adjustment of weighting coefficients and threshold values, as well as conducting experimental validation of the proposed model.