Secure and Privacy-Preserving SAP AI Frameworks for Predictive Business Intelligence in Cloud Environments

Abstract

Enterprises increasingly rely on SAP platforms deployed in cloud environments to support data-intensive business intelligence and predictive analytics. While artificial intelligence enhances decision-making capabilities, it also raises critical concerns related to data security, privacy, and regulatory compliance. This paper proposes a secure and privacy-preserving SAP AI framework designed to enable predictive business intelligence in cloud environments. The framework integrates advanced machine learning models with privacy-enhancing technologies to protect sensitive enterprise data throughout the analytics lifecycle. By leveraging secure data processing, controlled data access, and predictive intelligence, the proposed approach supports real-time insights across interconnected business processes. The framework is applicable to domains such as finance, healthcare, and supply chain management, where confidentiality and trust are paramount. Experimental evaluation demonstrates that the proposed solution maintains high predictive accuracy while significantly reducing data exposure risks. The results indicate that privacy-preserving AI can effectively enhance business intelligence without compromising security or operational efficiency in cloud-based SAP systems.