Threat-Aware Cloud Intelligence Framework Using SAP with Privacy-Preserving AI and Machine Learning for Secure Healthcare and Financial Systems
DOI:
https://doi.org/10.15662/IJEETR.2025.0706025Keywords:
Threat-Aware Cloud Intelligence, SAP Data Intelligence, Privacy-Preserving Artificial Intelligence, Machine Learning, Cloud Security, Intrusion Detection System, Healthcare Data Security, Financial Risk Management, AI-Driven Cybersecurity, Federated LearningAbstract
The rapid adoption of cloud-based platforms in healthcare and financial sectors has introduced significant challenges related to data security, privacy, and real-time threat detection. This paper presents a Threat-Aware Cloud Intelligence Framework Using SAP with Privacy-Preserving AI and Machine Learning for Secure Healthcare and Financial Systems. The proposed framework integrates SAP Data Intelligence with advanced machine learning techniques to enable intelligent data orchestration, secure analytics, and continuous threat monitoring across heterogeneous data sources. Privacy-preserving AI mechanisms, including data anonymization, federated learning, and secure model training, are employed to ensure regulatory compliance and protect sensitive patient and financial information.The framework incorporates an AI-driven Intrusion Detection System capable of identifying anomalous behavior and cyber threats in cloud environments. Additionally, collaborative AI components enhance decision-making by correlating operational data with contextual risk indicators across healthcare and financial domains. Experimental evaluation demonstrates improved threat detection accuracy, reduced false positives, and enhanced data privacy compared to traditional cloud security approaches. The proposed solution offers a scalable, intelligent, and compliant cloud intelligence architecture suitable for mission-critical healthcare and financial applications.
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