Secure SAP-Based AI and Cloud Lakehouse Platforms for Cyber-Resilient Healthcare Enterprise Systems
Abstract
The digital transformation of healthcare enterprises has led to the generation of massive volumes of heterogeneous data, demanding secure, scalable, and intelligent analytics platforms. This paper proposes a secure SAP-based AI and cloud lakehouse platform designed to support cyber-resilient healthcare enterprise systems. The proposed architecture integrates SAP Business Technology Platform, SAP HANA Cloud, and lakehouse-based data management with AI and machine learning models to enable unified processing of structured and unstructured healthcare data. Cybersecurity is strengthened through AI-driven threat detection, continuous monitoring, and policy-based access control to safeguard sensitive patient information and ensure regulatory compliance. The lakehouse architecture supports high-performance analytics while maintaining data consistency, governance, and interoperability across clinical and operational systems. Experimental evaluation demonstrates improved data processing efficiency, enhanced security resilience, and reliable analytics performance compared to traditional data warehouse and data lake solutions. The proposed platform provides a scalable and future-ready foundation for secure healthcare analytics and enterprise intelligence in cloud environments.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 5 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
14860-14868 |
Published |
October 15, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Felipe Rafael Azevedo (2024). Secure SAP-Based AI and Cloud Lakehouse Platforms for Cyber-Resilient Healthcare Enterprise Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 5 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 14860-14868. https://doi.org/10.15662/IJAESIT.2024.0705002 |
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