AI-NATIVE CLOUD ARCHITECTURES FOR INTELLIGENT ENTERPRISE SYSTEMS: A SCALABLE AND COMPLIANT FRAMEWORK FOR HEALTHCARE PLATFORMS

Purpose: This paper proposes a novel, holistic architectural framework for building AI-Native enterprise systems in the healthcare sector.It aims to address the critical dual challenge of achieving scalable, high-performance AI operations while ensuring stringent data security, privacy, and regulatory compliance. Design/methodology/approach:The research employs a design science research methodology.A conceptual framework is developed through a synthesis of contemporary cloud-native principles, AI/ML lifecycle management, and healthcarespecific regulatory requirements (e.g., HIPAA, GDPR).The framework is then validated through a simulated case study of a diagnostic imaging analytics platform, evaluating its scalability, compliance posture, and operational efficiency.Findings: The proposed "Health-Responsible AI Fabric" (HRAF) framework successfully integrates scalability drivers (e.g., microservices, serverless functions, MLOps) with compliance-by-design controls (e.g., data sovereignty, audit trails, encrypted compute).The simulation demonstrates that an AI-native approach can AI-Native Cloud Architectures for Intelligent Enterprise Systems: