Intelligent Emergency Response Architecture: A Cloud-Native, AI-Driven Framework for Real-Time Public Safety Decision Support

Abstract

Emergency response systems are undergoing rapid transformation as agencies confront rising incident volumes, increasingly complex emergencies, and growing expectations for real-time decision-making. Traditional 911 and PSAP infrastruc tures, built on monolithic, on-premises architectures struggle with fragmented data, limited processing speed, and operational inefficiencies that hinder timely and accurate response. This paper proposes a cloud-native, data-driven framework designed to unify telecom logs, geospatial data, PSAP records, and sensor streams into a scalable, resilient ecosystem capable of real-time intelligence generation. The framework integrates high-throughput data ingestion, microservices-based processing, zero-ETL patterns, and continuous AI inference to support incident classification, severity prediction, anomaly detection, and geospatial analysis. A decision support layer translates these insights into actionable dashboards, automated alerts, and optimized resource allocation. Implementation considerations including governance, privacy, interoperability, multi-region high availability, and disaster recovery ensure that the architecture aligns with regulatory and ethical constraints. The paper concludes with emerging research directions such as AI-enabled triage, federated data platforms, IoT-based emergency sensing, and autonomous response systems. Together, these advancements outline a path toward more anticipatory, intelligent, and trustworthy public safety operations.