Counter Terrorism

Use AI for intelligence, prediction, and autonomous counterterrorism operations worldwide

Business impact

• Operational efficiency — AI streamlines intelligence analysis and decision-making, improving operational workflows
• Response time — Faster detection and autonomous actions reduce time to respond to threats
• Intelligence accuracy — AI improves identification and prediction of terrorist activities and targets
• Targeting accuracy — Algorithmic targeting reduces errors and collateral damage in strikes
• Collateral damage reduction — Better discrimination minimizes harm to civilians and infrastructure
• Threat detection rate — AI increases the rate of identifying potential terrorist threats early
• Network dismantling success rate — Improved analytics help disrupt terrorist networks more effectively

Data requirements

• High-resolution drone imagery (Image) — Used for autonomous surveillance and target identification
• Sensor data from autonomous drones (Numeric) — Provides real-time environmental and threat information
• Big data from intelligence reports (Text) — Feeds AI models for threat prediction and network analysis
• Communication intercepts and social media (Text) — Monitored for propaganda and recruitment activities
• Operational logs and strike outcomes (Structured) — Used to refine AI targeting algorithms and effectiveness

AI methods and techniques

• Predictive AI — Forecasts terrorist activities and threat escalation from data patterns
• Generative AI — Generates threat scenarios and simulates countermeasures for planning
• Agentic AI — Enables autonomous drones to select and engage targets with minimal human input

AI models and model families

GPT-4, Claude, Custom deep learning models, Reinforcement learning agents

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: General Directorate Studies Documentation DGED, Israel Defense Forces, Libyan, US African Command, United States.