AI-Driven Law Enforcement in Hybrid/Multi-Cloud Environments: Balancing Innovation, Privacy, and Equity

Patil, Praneeth Kamalaksha (2025) AI-Driven Law Enforcement in Hybrid/Multi-Cloud Environments: Balancing Innovation, Privacy, and Equity. Open Access Research Journal of Engineering and Technology, 9 (1). 037-048. ISSN 2783-0128

The integration of artificial intelligence with hybrid/multi-cloud architectures presents a transformative framework for law enforcement agencies grappling with explosive growth in digital evidence. This article examines how these technologies enable agencies to manage vast quantities of data across distributed environments while maintaining security and compliance. The Edmonton Police Service case demonstrates tangible benefits through dramatically improved access times and significant cost reductions. Technical components including secure connectivity through VPN gateways, direct cloud connections, and federated learning methodologies allow agencies to collaborate without exposing sensitive information. Advanced implementations support predictive policing with privacy safeguards, real-time video analysis at network edges, and robust disaster recovery capabilities. The discussion addresses critical challenges including algorithmic bias, surveillance ethics, and digital divides between well-resourced urban departments and rural agencies. Experimental validation confirms substantial performance advantages in latency reduction, predictive accuracy, and cost efficiency compared to traditional infrastructures. Future directions point toward enhanced edge computing, augmented reality interfaces for officers, and broader social applications including preventative interventions and environmental protection, illustrating how these technologies can extend beyond enforcement to support community wellbeing when implemented with appropriate ethical frameworks.