AI-Driven Cloud Governance for Health Small Businesses: Zero-Trust Security, Autonomous Detection, and LDDR Cost Optimization through DevOps Integration

Lucía María Fernández Pérez

doi:10.15662/IJRAI.2025.0806806

Authors

Lucía María Fernández Pérez Systems Engineer, Spain Author

DOI:

https://doi.org/10.15662/IJRAI.2025.0806806

Keywords:

multi cloud, data integration, ERP, SAP S/4HANA, DevOps, Apache frameworks, zero touch automation, AI driven integration, real time streaming

Abstract

In today’s dynamic enterprise environment, organisations increasingly adopt multi‑cloud strategies to deploy large‑scale enterprise resource planning (ERP) systems such as SAP S/4HANA. However, the complexity of data integration across heterogeneous clouds, real‑time streaming data, legacy on‑premises systems, and distributed microservices poses significant challenges. This paper proposes an AI‑driven, zero‑touch DevOps model for multi‑cloud data integration, leveraging Apache open‑source frameworks (e.g., Apache Kafka, Apache Flink, Apache Airflow) combined with SAP S/4HANA’s native integration capabilities to create a scalable, automated pipeline for ERP data flows. The model emphasises automated provisioning, monitoring, metadata‑driven mapping, intelligent anomaly detection, and self‑healing workflows, eliminating manual integration tasks. Research demonstrates via prototype implementation and simulation that the proposed architecture reduces integration cycle times by up to 40 % and improves data consistency across clouds by 30 %. Key success factors include: unified semantic layer, containerised orchestration across clouds, AI‑based rule generation for data mapping, and standardised DevOps pipelines. The zero‑touch DevOps model supports continuous deployment of integration flows, enabling enterprises to adapt rapidly to process changes and cloud shifts. We highlight advantages (scalability, vendor independence, real‑time analytics) and disadvantages (complexity, skills demands, governance risk). The discussion addresses implications for ERP transformation, and concludes with future work on federated data mesh extension, enhanced trustworthiness of AI mappings, and compliance in global multi‑cloud deployments.

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