(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2023)
The rapid digital transformation of enterprise ecosystems has accelerated the adoption of cloud-native architectures and artificial intelligence (AI) technologies across critical sectors such as healthcare, regulatory compliance, and cybersecurity. However, the integration of AI within distributed cloud environments introduces new security, governance, and ethical challenges. This paper explores the design and implementation of secure AI-driven cloud-native enterprise platforms tailored for compliance automation, healthcare analytics, and cyber defense applications. The study examines architectural frameworks leveraging microservices, containerization, zero-trust security models, and automated governance mechanisms to ensure regulatory adherence and operational resilience. In healthcare analytics, AI models deployed within secure cloud infrastructures enable predictive diagnostics, patient risk stratification, and operational optimization while maintaining strict compliance with regulatory standards such as HIPAA and GDPR. In cyber defense, AI-powered anomaly detection and threat intelligence systems enhance proactive security capabilities. The research further proposes a comprehensive methodology for developing scalable, secure, and compliant enterprise platforms by integrating DevSecOps, privacy-preserving machine learning, and policy-driven orchestration. The paper concludes by outlining advantages, limitations, and future research directions toward trustworthy, explainable, and autonomous enterprise AI ecosystems.