To enable a deeper understanding of the complex phenomenon of .mental wars,. a modeling methodology has been developed that integrates the capabilities of modern large language models (LLMs) and graph theory. The primary objective of the study is to transform the existing model by enhancing its depth and detail, thereby revealing the essence, mechanisms, strategic approaches, and consequences of mental wars within the context of hybrid warfare. To achieve this goal, a comprehensive analytical toolkit was applied, including semantic network analysis, modularity-based clustering, and node ranking within graphs. The use of generative artificial intelligence adds particular value, as it not only automatically generates new concepts but also uncovers logical relationships among them . significantly deepening the analysis and enabling a holistic understanding of the architecture of mental warfare. The outcome is an expanded dynamic model . a network of interrelated elements covering the key dimensions of mental wars: their strategic objectives, instruments of influence, key actors, implementation mechanisms, and anticipated consequences. This model serves as an effective analytical tool for investigating informationpsychological operations, predicting their impact, and developing efficient countermeasures in the domain of information security.

Keywords

Mental war, hierarchical model, AI, LLM, clustering, visualization, information security1