Zero-Trust Security Models for AI-Powered Healthcare Systems in Multi-Cloud Architectures
DOI:
https://doi.org/10.15662/IJARCST.2022.0506014Keywords:
Zero Trust Architecture, AI Healthcare Systems, Multi-Cloud Security, Cloud Computing, Identity Management, Micro-Segmentation, Healthcare Cybersecurity, Adaptive Access ControlAbstract
The rapid integration of artificial intelligence (AI) into healthcare systems has transformed diagnostics, predictive analytics, personalized medicine, and operational management. These AI-powered healthcare applications increasingly operate in multi-cloud environments, leveraging distributed infrastructures offered by providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. While multi-cloud architectures enhance scalability, redundancy, and flexibility, they introduce complex security challenges, including identity sprawl, inconsistent policy enforcement, lateral movement risks, and expanded attack surfaces. Traditional perimeter-based security models are insufficient in such decentralized ecosystems.
Zero-Trust Security Models (ZTSMs) offer a robust alternative by enforcing continuous verification, least-privilege access, and micro-segmentation across distributed systems. This research proposes a comprehensive zero-trust framework tailored for AI-powered healthcare systems deployed in multi-cloud environments. The framework integrates AI-driven identity analytics, adaptive risk scoring, secure API gateways, encrypted data pipelines, policy orchestration, and continuous compliance monitoring. The study outlines architectural components, implementation methodology, performance evaluation metrics, and regulatory considerations. Findings indicate that zero-trust models significantly enhance data confidentiality, integrity, and availability while supporting AI scalability and interoperability. The proposed approach strengthens healthcare resilience against evolving cyber threats in complex cloud ecosystems.
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