DYNAMIC ACCESS CONTROL MANAGEMENT BASED ON BEHAVIORAL MODELS OF USERS AND SERVICES

Abstract

This article proposes an approach to dynamic access rights management based on the analysis of behavioral models of interacting subjects. Particular attention is given to behavioral characteristics that can serve as signals for adapting access policies, methods of their formalization, and algorithmic strategies for real-time decision-making. The study provides an overview of scientific approaches to modeling the behavior of users and services, as well as highlights the limitations of traditional access control systems that rely on statically defined rules or attributes. In particular, issues such as excessive privileges, slow response to contextual changes, and the absence of automated mechanisms for policy evolution in response to real interaction scenarios are analyzed. The paper identifies the urgent scientific problem of developing effective and anomaly-resistant mechanisms for behavior-oriented adaptation of access rights capable of supporting a continuous cycle of monitoring, analysis, and policy adjustment. The structure of behavioral models, methods of their training and interpretation, and principles of integrating such models into access control systems are discussed with consideration of scalability requirements, signal reliability, and risk minimization. Additionally, the article outlines the importance of these approaches for enhancing information system resilience to insider threats, as well as the potential use of behavioral characteristics as a basis for predicting harmful activity and constructing more flexible and self-adaptive access management mechanisms.