Knowledge Modelling for Automated Risk Assessment of Cybersecurity and Indirect Patient Harms in Medical Contexts

Abstract

The use of connected medical and in vitro diagnostic devices (CMD&IVD) as part of individual care and self-care practices is growing. Significant attention is needed to ensure that CMD&IVD remain safe and secure throughout their lifecycles — as if a cybersecurity incident were to occur involving these devices, it is possible that in some cases harm may be brought to the person using them. For the effective safety management of these devices, risk assessment is needed that covers both the cybersecurity and patient safety domains. To this end, we present knowledge modelling of indirect patient harms (e.g., misdiagnosis, delayed treatment etc.) resulting from cybersecurity compromises, along with a methodology for encoding these into a previously developed automated cybersecurity risk assessment tool, to begin to bridge the gap between automated risk assessment related to cybersecurity and patient safety.

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