AI-enhanced monitoring for law enforcement security systems

Abstract

Law enforcement equipment failures during critical operations create immediate risks to officer safety and public security. Traditional monitoring relies on reactive maintenance - fixing problems after they occur. This paper presents a monitoring framework that combines Prometheus and Thanos infrastructure with three machine learning techniques: adaptive Isolation Forest for anomaly detection, LSTM networks with attention mechanisms for failure prediction, and reinforcement learning for parameter optimization. The system was validated through comprehensive cloud-based simulation modeling six months of operations across 1,847 synthetic monitoring points representing radio systems, surveillance cameras, and vehicle equipment. Simulation parameters were derived published equipment failure statistics and validated through limited pilot deployment (87 endpoints, 3 months) with one partner law enforcement agency. Results show F1-score improvement 0.72 to 0.89, detection time reduction 147 to 41 seconds (72% faster), and false positives dropping 12.3% to 3.8%. LSTM models predicted equipment failures 4-8 hours in advance with 87% average accuracy across five equipment categories. The framework scaled linearly 50 to 3,000+ endpoints with detection latency under 52ms. However, several challenges remain: simulation cannot capture all real-world complexity, integration requires custom interfaces for legacy systems, and operators need enhanced explainability features to trust AI recommendations. While primary findings are based on simulated data, results suggest promising directions for operational systems pending comprehensive real-world validation.