Unified AI and SAP Enterprise Technologies for Cloud Security Intelligent Automation and Data Engineering Solutions

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Sami Honkonen

Abstract

The integration of Artificial Intelligence (AI) with SAP enterprise technologies has become a transformative approach for organizations seeking secure, intelligent, and scalable digital infrastructures. Modern enterprises increasingly depend on cloud computing, data analytics, and automated business operations to improve efficiency and maintain competitive advantage. SAP technologies combined with AI-driven systems enable advanced cloud security, predictive analytics, intelligent automation, and efficient data engineering solutions. This study examines the role of unified AI and SAP enterprise technologies in strengthening enterprise cloud ecosystems and enhancing organizational performance. The research explores how AI-powered SAP platforms support cybersecurity management through anomaly detection, automated threat response, and predictive risk analysis. In addition, the study highlights the importance of intelligent automation using machine learning and robotic process automation to optimize business workflows and reduce operational complexity. The research further analyzes the role of SAP HANA, SAP Business Technology Platform, and AI-driven analytics in improving enterprise data engineering and real-time decision-making. A qualitative research methodology based on literature review and thematic analysis is adopted to evaluate current industry practices and technological developments. The findings indicate that AI-integrated SAP systems improve operational agility, data intelligence, cybersecurity resilience, and enterprise productivity while also presenting challenges related to integration complexity, ethical concerns, and workforce skill gaps.

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How to Cite

Unified AI and SAP Enterprise Technologies for Cloud Security Intelligent Automation and Data Engineering Solutions. (2022). International Journal of Humanities and Information Technology, 4(01-03), 193-202. https://doi.org/10.21590/

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