Abstract

This study examines the integration of Artificial Intelligence (AI) and cloud lakehouse cybersecurity within SAP environments, focusing on healthcare imaging systems and fraud-resilient broadband web services. Healthcare imaging produces massive volumes of sensitive data, requiring advanced storage, processing, and security solutions to support clinical workflows while maintaining compliance. Meanwhile, broadband web systems face increasing threats of fraud, identity theft, and network intrusion. Cloud lakehouse architecture, combining data lake scalability with data warehouse reliability, provides a unified platform for handling structured and unstructured data, enabling AI-based analytics and cybersecurity monitoring. By embedding AI models for anomaly detection, predictive analytics, and automated response, organizations can enhance threat detection and operational efficiency. SAP systems serve as enterprise backbones, enabling integrated workflows across clinical, administrative, and financial functions. This study explores how AI-driven cybersecurity can be deployed in cloud lakehouses integrated with SAP, emphasizing design considerations, data governance, performance, and compliance. The research highlights benefits such as improved detection, scalability, and compliance, while addressing challenges including data quality, system complexity, and ethical concerns. The findings suggest a balanced approach that prioritizes security, privacy, and operational continuity while leveraging AI innovation.