Zero-Trust Architecture for Smart Hospitals: A Virtual Blueprint for Cyber-resilient Healthcare Infrastructure
The rapid digital transformation of healthcare through smart hospitals driven by AI, IoMT, cloud computing, and telemedicine has heightened cyber vulnerabilities, with 276 million records breached globally in 2024. This study developed a Zero Trust Architecture (ZTA) blueprint to strengthen cybersecurity in smart hospitals, addressing the challenges of diverse device ecosystems and regulatory compliance. Drawing on a comprehensive literature review, the research established ZTA’s theoretical foundation, emphasizing continuous verification rather than traditional perimeter defenses. The study is broadly applicable and applied a Design Science Research approach and mixed-methods analysis, combining risk models, maturity assessments, and machine learning for IoMT threat detection. Results showed significant improvements: a two-thirds reduction in cyber risks, over 95% accuracy in detecting IoMT threats, strong compliance with HIPAA requirements, and a threefold return on investment. The blueprint proved scalable across different hospital types, though limitations include reliance on simulated datasets. Recommendations highlight the need for tailored IoMT datasets, integration of explainable AI, real-world deployment, standardized metrics through collaboration, and adaptive algorithms for evolving threats. Overall, this research provides a practical and evidence-based framework to enhance the resilience of smart hospitals, safeguard patient safety and ensure operational continuity.
Authors
- Valerie Ojinika Ejiofor (ORCID: https://orcid.org/0009-0009-2206-1377)
- Faith Hauwa Oluwapamilerin Kolo
- Emonena Patrick Obrik-Uloho (ORCID: https://orcid.org/0009-0008-2275-9583)
- Chukwudalu Henry Egonwanne
- Rukayat Oluwabukola Olasege
Institutions
- University of Ottawa (CA)
- Prairie View A&M University (US)
- Fairleigh Dickinson University (US)
- Toronto Zoo (CA)
- University of Tampa (US)
- Ottawa University (US)
Publication Details
- Journal
- Archives of Current Research International
- Published
- 2025-10-13
- DOI
- https://doi.org/10.9734/acri/2025/v25i101557
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 2.28