Yukhno Alexander S.

The digitalization of public administration has resulted in an increase in cyber dangers; nevertheless, current approaches to risk management remain fragmented and do not allow for systematic supervision. A thorough examination of Russian and international studies and reviews in the field of cybersecurity has allowed us to organize the current cyber risks facing the state, develop a basic framework for managing them, and create a unified digital system for internal control and risk management, which can serve as the foundation for a nationwide cybersecurity system. This system can provide real-time cyber threat monitoring, automate decision-making, and enhance the overall resilience of government systems. Future research on adaptive cybersecurity systems can use the study’s findings as a theoretical and methodological basis. The conclusions can also be used by businesses that need to safeguard vital information infrastructure and by government organizations developing cybersecurity initiatives.

The article introduces the Russian school of management (RSM) as a comprehensive system of knowledge and practices consistent with Russia’s cultural and historical identity, as well as its contemporary geopolitical challenges. The authors have identified and systematized the stable elements of the Russian management model: a core set of values and meanings, priority of the common good, mobilization flexibility, paternalism, and a synthesis of tradition and innovation. The authors formulated 10 key postulates of the RSM based on an analysis of historical practices and the best domestic schools. The study overcomes the fragmented nature of research on Russian management traditions, presenting them as a holistic, historically continuous phenomenon with a unique ideocratic paradigm.

As data volumes grow rapidly, effectively employing data is becoming critically important for enhancing the performance of public administration. However, existing approaches do not always take into account the institutional, technological, and regulatory aspects of working with Big Data. This leads to fragmentation and reduced manageability. One particular challenge is the lack of a unified, methodologically sound framework adapted for public administration. Using a systematic analysis and comparative approach, the author developed a typology of Big Data management models. This typology considers departmental characteristics, technological development levels, and national digital transformation priorities. The results of the study include the following: justification of the key characteristics of Big Data as a tool for improving the efficiency of public administration; development of methodological foundations for building an effective data management system; development of a typology of data management models that optimizes interdepartmental interaction and improves the quality of management decisions. The article’s practical significance lies in its proposal of specific measures for integrating disparate data. These measures include system inventory, process standardization, and the creation of a single point of integration for fragmented data. Government authorities can use the study’s findings in the formulation of digital transformation strategies, the establishment of a regulatory framework, and the development of digital infrastructure. The proposed solutions will make data management in public administration more transparent, secure, and efficient.

The article presents and tests a comprehensive methodology for analyzing the strategic planning documents of the Russian Federation. It studies how traditional Russian spiritual and moral values are implemented in these documents. This model facilitates the development of methodological approaches to identifying the value foundations of society and state development in strategic planning documents. The methodology also enables the creation of analytical instruments to identify value categories and establish semantic links between them and other elements of strategic planning. The authors’ approach overcomes the limitations of content analysis, moving us towards constructing a comprehensive semantic map.

The authors evaluated the demand for AI solutions in the public sector and the AI technology available on the market using data from the Russian software registry and a comparative examination of the types of AI technologies employed at the federal and local levels. The study confirms that: (a) there is a correlation between the structure of AI solutions and public sector demand; (b) federal and municipal governments have quite different priorities for implementing AI; and (c) there is a need to standardize approaches, replicate successful practices, and remove technical, organizational, and legal barriers to scaling AI in public administration. Based on the compatibility of the solutions’ structure with the needs of the public sector, the authors believe that the domestic AI industry has reached a high level of maturity. At the same time, the accelerated introduction of AI into the public administration system highlights the need to develop unified regulatory frameworks, adapt organizational structures to new technological realities, and create a comprehensive trust infrastructure for algorithmic decision-making.