Designing Compliant and Explainable AI for Cloud-Native Public Safety Frameworks

Abstract

Public safety organizations increasingly rely on artificial intelligence to support emergency response, risk assessment, and operational decision making. While cloud-native platforms offer scalability and resilience, the integration of AI into public safety systems raises significant challenges related to compliance, transparency, and trust. This paper presents a design framework for compliant and explainable AI within cloud-native public safety architectures. The proposed approach combines explainability mechanisms, governance controls, and distributed system patterns to support accountable AI-driven decision support under operational stress. Empirical evaluation demonstrates that explainable and compliant AI services can be deployed at scale without degrading system performance or response time.