A risk and conformity assessment framework to ensure security and resilience of healthcare systems and medical supply chain

In recent years, the healthcare sector has undergone a significant digital transformation, driven by the rise of the Internet of Medical Things and the exponential use of connected medical devices in healthcare service delivery. This transformation offers numerous benefits, including enhanced patient data collection, processing, and informed treatment decisions. Despite these advantages, digital adoption brings several security challenges that pose considerable risks to overall healthcare service delivery. Additionally, connected medical devices must comply with sector-specific regulatory requirements to ensure trustworthiness and facilitate their broader adoption in the healthcare sector. There is, therefore, a pressing need to understand and manage these risks and compliance issues to secure and strengthen the resilience of healthcare systems. This work addresses these needs by introducing a novel Risk and Conformity Assessment Framework and Certification Scheme, implemented within an agile Information Security Management System context to enhance the security and resilience of healthcare systems. The framework leverages Artificial Intelligence (AI) in risk management practices, improving security assessments, risk prediction, security control implementation, and continuous monitoring. AI algorithms analyze large data volumes from various sources, enabling efficient processing and the identification of potential risk patterns. Additionally, AI-driven automation tools ensure consistent deployment of security controls, while continuous AI monitoring detects abnormal activities and enables rapid response to security incidents. The proposed Cybersecurity Certification Scheme incorporates AI-based security assessments into the certification process, facilitating efficient conformity assurance. This scheme also promotes a collaborative approach with relevant regulatory bodies to achieve compliance. While this work introduces a conceptual framework, its implementation and potential refinements remain subjects for future research. Further studies are necessary to validate its effectiveness, enhance its components, and evaluate its practical application in real-world healthcare environments.