Design of Secure and Fault-Tolerant Patterns for AI-Driven Healthcare Analytics in Hybrid Cloud Platforms

Andreas John Petrovic

doi:10.15662/IJRAI.2025.0806024

Authors

Andreas John Petrovic Senior Project Lead, Madrid, Spain Author

DOI:

https://doi.org/10.15662/IJRAI.2025.0806024

Keywords:

Artificial Intelligence, Healthcare Systems, Hybrid Cloud Computing, Fault Tolerance, Secure Architecture, System Reliability, Data Privacy

Abstract

The adoption of artificial intelligence in healthcare systems has significantly improved clinical decision-making, operational efficiency, and patient outcomes. However, the deployment of AI-driven healthcare applications in hybrid cloud platforms introduces critical challenges related to security, fault tolerance, and system reliability. This paper presents the design of secure and fault-tolerant patterns for AI-driven healthcare systems operating in hybrid cloud environments. The proposed design integrates resilience mechanisms such as redundancy, automated failover, and consensus-based coordination with security controls including encryption, access control, and policy enforcement. Hybrid cloud orchestration enables seamless workload distribution across on-premises and cloud resources while ensuring data availability and regulatory compliance. The design supports continuous AI model execution and real-time healthcare analytics even under partial system failures or cyber threats. Experimental evaluation demonstrates improved system availability, reduced recovery time, and enhanced protection of sensitive healthcare data. The results highlight the effectiveness of combining fault tolerance and security patterns to build robust AI-enabled healthcare platforms in hybrid cloud settings.

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