Data-Driven Approaches and Artificial Intelligence Applications in Security Research: Methodological Innovations and Future Perspectives

Abstract:

Data-driven methodologies and artificial intelligence are reshaping security research by enabling systematic, large-scale analysis of complex threat environments. Quantitative event data combined with statistical and network-based approaches allow for the identification of temporal and structural patterns in violent activity while remaining sensitive to definitional and reporting limitations. Advances in natural language processing, particularly large language models (LLMs), support thematic text mining, concept identification, and trend detection across extensive textual corpora, with human validation ensuring analytical reliability. Visualization techniques such as timelines, geo-spatial representations, and interactive dashboards enhance interpretability and communication of findings. Emerging directions include multimodal data fusion integrating textual, visual, and numerical sources, and predictive analytics aimed at early warning under conditions of uncertainty. Ethical and governance considerations, bias, privacy, transparency, and adversarial manipulation remain central to responsible research, reinforcing the importance of reproducibility, openness, and methodological rigor in contemporary security studies