Abstract

The convergence of Artificial Intelligence (AI) with cloud lakehouse architectures presents a transformative approach to enterprise security, analytics, and operational intelligence. This study proposes a unified AI and cloud lakehouse security model specifically designed to integrate SAP financial fraud prevention with medical image intelligence in high-speed broadband web applications. The model leverages AI-driven anomaly detection, predictive analytics, and automated response mechanisms to protect SAP financial systems from fraud while simultaneously enabling intelligent analysis of medical imaging data. The cloud lakehouse provides a scalable, unified repository capable of handling structured SAP transaction data, semi-structured logs, and unstructured imaging files. High-speed broadband environments add real-time streaming and low-latency requirements, necessitating efficient data ingestion, processing, and security monitoring. The proposed model emphasizes strong data governance, encryption, identity access management, and continuous auditing to maintain compliance with regulations such as GDPR and HIPAA. By integrating SAP, AI, and cloud lakehouse security, organizations can achieve proactive fraud detection, improved clinical insights, and enhanced system resilience. The study discusses implementation challenges, including data quality, model bias, and integration complexity, and provides a framework for evaluating performance and security outcomes.