Zero-Trust–Based Enterprise Cloud AI Architecture for Secure and Privacy-Preserving Healthcare CNN Systems
DOI:
https://doi.org/10.15662/IJARCST.2025.0805026Keywords:
Zero Trust Architecture, Healthcare AI, Convolutional Neural Networks, Secure Cloud Computing, Privacy Preservation, Enterprise Architecture, MLOps, Regulatory ComplianceAbstract
The rapid adoption of Convolutional Neural Networks (CNNs) in healthcare has significantly improved diagnostic accuracy, medical image analysis, and clinical decision support. However, deploying CNN-based healthcare AI systems in cloud environments introduces substantial security and privacy risks due to the sensitive nature of patient data and the complexity of distributed infrastructures. Traditional perimeter-based security models are insufficient to address modern cyber threats, insider risks, and regulatory requirements. This paper proposes a zero-trust–based enterprise cloud AI architecture designed to ensure secure and privacy-preserving deployment of healthcare CNN systems. The proposed architecture integrates zero-trust security principles across business, application, data, and infrastructure layers, enforcing continuous authentication, least-privilege access, and real-time monitoring. It incorporates secure data ingestion, cloud-native MLOps pipelines, encrypted model lifecycle management, and compliance-aware governance mechanisms aligned with healthcare regulations such as HIPAA and GDPR. By embedding zero-trust concepts into enterprise architecture, the proposed framework mitigates attack surfaces while maintaining scalability and operational efficiency. This work provides a holistic blueprint for healthcare organizations seeking to operationalize CNN-based AI systems in cloud environments without compromising data confidentiality, integrity, or availability
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