OSINT-РОЗСЛІДУВАННЯ: МОДЕЛЬ ЦИКЛУ РОЗВІДКИ ТА МЕТОДИ ПОБУДОВИ МІЖОБ’ЄКТНИХ ЗВ’ЯЗКІВ

This article focuses on addressing the task of improving the effectiveness of OSINT investigations, particularly in the context of the war in Ukraine. The study aims to analyze and enhance existing methods and tools for establishing inter-object relationships in the field of intelligence data collection. As a result of examining modern OSINT (Open Source Intelligence) technologies and their capabilities, Gibson’s intelligence cycle model was chosen as the baseline. The well-known six-level Gibson intelligence cycle model has been analyzed and expanded, and an improvement is proposed by introducing the method of inter-object relationships during the data collection process. The proposed inter-object relationships method allows for structuring and monitoring the interconnections among the main OSINT entities (person, place, organization, event, action, product/system), thereby enabling a comprehensive evaluation of heterogeneous information. Particular attention is paid to the “person” entity, for which a detailed scheme of applying the inter-object relationships method has been developed, depending on the available input parameters (name, alias, address, phone number, email, etc.). The practical significance of this scheme is evidenced by the increased effectiveness of search operations, more flexible search settings, and a reduced risk of overlooking critical information. The approaches described are relevant to military intelligence, law enforcement agencies, investigative journalism, and corporate security. The presented results may serve as a foundation for expanding the data sources, automating the inter-object relationships process using artificial intelligence, and developing intelligent dashboards for real-time information visualization and monitoring. The research is based on analytical work with up-to-date literature sources and various types of information resources, as well as on the practical application of OSINT methods during intelligence data collection.