Artificial Intelligence Integration for Modern Policing and Public Security Enhancement

Abstract

The integration of Artificial Intelligence (AI) into modern policing has transformed crime prevention, detection, and investigative mechanisms across multiple jurisdictions. This study examines the technical architectures, operational impacts, and analytical outcomes of AI-enabled policing systems, including predictive policing models, automated surveillance analytics, and digital forensic algorithms. Using a mixed-methods design, the research incorporates simulated police datasets, spatial–temporal crime mapping, machine-learning model evaluations, and qualitative insights from law-enforcement practitioners. Findings indicate that AI-driven predictive models improve hotspot forecasting accuracy by up to 87%, while computer-vision-based surveillance increases anomaly-detection precision to 92%. AI-assisted digital forensics significantly enhances data extraction quality and investigative efficiency. However, challenges emerge regarding algorithmic transparency, data biases, privacy concerns, and unequal access to high-performance computing infrastructures. The study concludes that the effective adoption of AI in policing requires standardized governance frameworks, interoperable data architectures, and strong public oversight. This research contributes to the scientific understanding of AI-policing ecosystems by presenting a comprehensive technical assessment, identifying socio-ethical implications, and proposing an integrated implementation roadmap for sustainable public-security enhancement.