Enhancing Risk Management on IoT Medical Devices

Abstract

The Internet of Medical Things (IoMT) represents a transformative step in healthcare, enhancing patient outcomes through real-time monitoring and treatment. However, the increasing reliance on IoMT devices exposes critical vulnerabilities, posing significant risks to patient safety and healthcare operations. This paper evaluates the cybersecurity challenges of IoMT devices and explores how the DYNAMO tools (Cyber-Attack Forecasting, Secure AI, ThreatLens, and CTI Extractor) address these threats. A phased research approach was employed, with a literature review, DYNAMO tool analysis, and vulnerability-tool mapping. The results demonstrate the potential of advanced AI-driven tools to predict, detect, and mitigate threats, ensuring robust security for IoMT ecosystems. The research found promising results but acknowledges limitations due to the theoretical nature of the analysis. Without practical testing, the feasibility of these tools in real-world IoMT environments remains uncertain. For instance, CAF and CTI Extractor rely heavily on accurate and comprehensive data, which may not always be available in healthcare organisations. Secure AI's computational demands and ThreatLens's complex visualisations may present barriers, particularly for smaller healthcare facilities with limited resources or expertise.