ARTIFICIAL INTELLIGENCE SUPPORTED FIELD RECOGNITION OF BIOLOGICAL, CHEMICAL AND ENVIRONMENTAL THREATS IN WARTIME

Abstract

Wartime biological, chemical and environmental threats increasingly arise from damaged industrial infrastructure, disrupted public-health systems, contaminated water networks, fires, hazardous transport corridors and ambiguous hostile actions. Predictive prevention is defined here as the ability to identify probable escalation pathways from incomplete and time-sensitive signals before full incident confirmation is available. This article develops an operational model for uniformed services and civil protection actors that combines multi-source data, provenance control, anomaly detection, unmanned stand off reconnaissance, dynamic geospatial risk mapping, artificial intelligence (AI) staff-support modules and exercise-based validation. The model does not transfer decisions to AI. It uses AI to rank information gaps, expose uncertainty, structure reports and support safer reconnaissance, while legally responsible human command remains accountable for warning, sampling, isolation, evacuation and stand-down decisions. The practical value of the framework is earlier recognition of weak signals, reduced responder exposure, better prioritization of sampling and more auditable command reasoning during wartime emergencies.