This study aims to deepen open-source intelligence (OSINT) analysis of Ukraine's Defense Intelligence International Corps through artificial intelligence methods, exploring AI's application potential and methodological value in complex warfare information environments. The core objectives address two questions: First, how can AI technologies be effectively integrated into the OSINT cycle to enhance information screening, pattern recognition, and risk prediction? Second, can AI-driven OSINT provide more forward-looking and systematic support for strategic decision-making? Methodologically, this study adopts a multidisciplinary mixed methodology, integrating text metrology, semantic network analysis, risk radar modeling, and time-series projection to form a comprehensive framework: “Data Collection → AI Processing → Risk Assessment → Timeline Analysis → Insight Output.” The research process extensively leverages multilingual datasets (English, Ukrainian, Russian) and cross-platform information sources (official, media, social networks), utilizing visualization modeling to present data and risks in multidimensional formats. Results demonstrate that AI significantly enhances the depth and breadth of information processing in OSINT analysis. It outperforms traditional methods in misinformation detection accuracy, multilingual keyword extraction efficiency, and predictive power for risk patterns. Military risks and information warfare risks were assessed as highest priority, followed by public opinion risks and legal risks, revealing an overall “military-information warfare-public opinion” triple-high-risk configuration. Concurrently, time-series analysis revealed rhythmic patterns in risk evolution, providing quantitative foundations for future strategic planning. The study concludes that AI not only transforms OSINT's technical framework but also propels it toward structured, systematic, and forward-looking intelligence generation. AI-driven OSINT effectively bridges the tension between data fragmentation and systematic strategic analysis, enabling a qualitative leap in the intelligence cycle from “information accumulation” to “strategic insight.” This study provides an empirical paradigm for interdisciplinary research at the intersection of artificial intelligence and intelligence studies, holding significant theoretical and practical implications for future military conflicts, national security, and policy formulation.
Wei Meng (Mon,) studied this question.