A Secure-by-Design Cloud-Native Framework for Intelligent Healthcare Analytics Integrating Deep Learning and Generative AI for Waste Optimization and Cybersecurity
DOI:
https://doi.org/10.15662/IJEETR.2024.0604005Keywords:
Cloud-Native Architecture, Secure-by-Design, Healthcare Analytics, Deep Learning, Generative AI, Cybersecurity, Waste Optimization, Zero Trust, MLOps, Intelligent SystemsAbstract
Healthcare systems increasingly rely on cloud-native architectures and artificial intelligence to improve operational efficiency, clinical decision-making, and cybersecurity resilience. However, the rapid digitalization of healthcare data introduces challenges related to data privacy, cyber threats, and inefficient resource utilization, including medical waste and redundant computational workloads. This paper proposes a secure-by-design cloud-native framework that integrates deep learning and generative AI to enable intelligent healthcare analytics with a focus on waste optimization and cybersecurity. The framework leverages containerized microservices, zero-trust security principles, encrypted data pipelines, and AI-driven orchestration to ensure scalability, reliability, and regulatory compliance. Deep learning models are employed for predictive analytics and anomaly detection, while generative AI supports data augmentation, intelligent reporting, and automated threat modeling. Experimental analysis and architectural evaluation demonstrate that the proposed framework enhances data security, reduces operational waste, and improves system responsiveness compared to traditional cloud deployments. The framework provides a robust foundation for next-generation healthcare platforms that require intelligent automation, secure data handling, and sustainable resource management.
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