Abstract

The rapid adoption of cloud-native enterprise platforms in healthcare has significantly increased exposure to sophisticated cyber threats, necessitating intelligent and risk-aware security mechanisms. This paper proposes an AI-powered, risk-based cyber defense framework for secure SAP-enabled cloud-native healthcare enterprises. The proposed approach integrates SAP Business Technology Platform with machine learning and generative AI models to continuously assess cyber risks, analyze enterprise telemetry, and detect threats across distributed cloud environments. A lakehouse-based data architecture is employed to unify structured and unstructured security, operational, and clinical data, enabling scalable analytics and real-time threat intelligence. Risk-based prioritization mechanisms dynamically evaluate vulnerabilities, user behavior, and system configurations to enable proactive threat detection and response. The framework enhances healthcare cybersecurity by supporting automated anomaly detection, contextual threat reasoning, and compliance-aware security controls while preserving data confidentiality and integrity. Experimental evaluation demonstrates improved detection accuracy, reduced response time, and enhanced cyber resilience compared to conventional rule-based security systems. The proposed solution offers a scalable and intelligent foundation for protecting cloud-native SAP healthcare enterprises against evolving cyber threats.