Models of data processing and logical access segregation considering the heterogeneity of entities in information systems

Abstract

The subject of the research is the process of logical access segregation to data in information systems. The aim of the article is to improve the accuracy and reliability of modeling processes for data processing and logical access segregation considering the heterogeneity of entities in information systems. The tasks to be solved include: conducting a comparative analysis of modern data access distribution models, integrating simpler role-based models, synthesizing hierarchical role-based models, developing enforced typing models based on trust relationships, and presenting the main provisions of the security policy integration process. The methods used are: systems analysis, component design, logical and simulation modeling in the form of role-based access segregation models. The results obtained include: development of data processing models and logical access segregation in information systems that take into account the heterogeneity of entities and the multi-level structure of information systems. The models differ from known ones by considering the heterogeneity of entities and the multi-level structure of information systems. This has increased scalability by up to 35% due to a modular approach to defining security policies. Additionally, the developed model demonstrates 25% higher implementation practicality as it easily integrates with existing access control systems and adapts to various platforms and environments. The proposed models are effective for large information systems and distributed environments due to their modularity and ability to adapt to different operational conditions. This ensures reliable access control in systems with numerous subjects and objects. The implementation of multi-level RBAC models has improved the accuracy and reliability of results.