Privacy-Preserving SAP-Based Analytics for Healthcare Applications and Wastewater Management Using LLMs and Cloud Encryption

Abstract

The rapid adoption of SAP-based enterprise systems in financial and healthcare sectors has intensified concerns related to data security, privacy preservation, and regulatory compliance. The integration of Large Language Models (LLMs) into cloud-enabled SAP environments introduces powerful capabilities for intelligent automation, decision support, and business process optimization, while simultaneously expanding the attack surface. This study proposes a secure and privacy-preserving framework that combines LLM-driven intelligence with robust cloud encryption mechanisms to protect sensitive financial and healthcare data throughout its lifecycle. The architecture incorporates end-to-end encryption, role-based access control, secure key management, and policy-aware LLM orchestration to ensure compliance with standards such as HIPAA, GDPR, and PCI-DSS. By enabling encrypted data processing and controlled semantic inference, the framework minimizes data exposure while maintaining operational efficiency and analytical accuracy. Experimental evaluation demonstrates improved threat detection, reduced data leakage risk, and enhanced system resilience without significant performance overhead. The proposed approach highlights the feasibility of deploying LLMs in mission-critical SAP environments while achieving strong security guarantees and data privacy assurance.