A Cloud-Native AI-Driven SAP-Centric Architecture for Real-Time Decision Intelligence in Public Safety and Enterprise Operations
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Abstract
Public safety agencies and enterprise organizations increasingly operate in environments characterized by high data velocity, complex operational dependencies, and stringent regulatory requirements. Traditional on-premise systems and siloed architectures often fail to provide the real-time insights required for timely decision-making. This paper proposes a cloud-native, AI-driven, SAP-centric architecture designed to deliver real-time decision intelligence across public safety and enterprise operations.
The proposed framework integrates SAP S/4HANA and SAP Business Technology Platform (BTP) with cloud-native artificial intelligence services, including machine learning, predictive analytics, and Generative AI models. Real-time data ingestion pipelines process structured enterprise transactions and unstructured data such as incident reports, sensor feeds, logs, and social signals. Predictive analytics models support risk forecasting, resource optimization, and anomaly detection, while Generative AI enables automated summarization, situational reporting, and conversational decision support.
SAP serves as the digital core, ensuring transactional consistency, governance, and compliance, while cloud-native microservices provide scalability, resilience, and rapid innovation. Security and compliance are embedded through identity management, encryption, audit logging, and explainable AI mechanisms, aligning with public safety regulations and enterprise governance standards.
The architecture demonstrates how public safety systems and enterprise operations can be unified within a single intelligent framework, enabling coordinated response, operational efficiency, and data-driven strategy execution. The proposed approach highlights the role of SAP-centric cloud architectures in enabling trusted AI adoption and real-time decision intelligence in mission-critical environments.
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