Threat Artificial Intelligence and Cyber Security in Health Care Institutions

Fernandes, Ana; Figueiredo, Margarida; Carvalho, Filomena; Neves, José; Vicente, Henrique

doi:10.1007/978-3-030-72236-4_13

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…Fernandes et al investigated the use of techniques based on Threat Artificial Intelligence, Chaos, Entropy and Security (TAICE&S) for solving cybersecurity problems in cryopreservation laboratories [ 71 ]. Their research aimed to address General Data Protection Regulation (GDPR) issues in this type of laboratory using techniques derived from the relationship between TAICE and cybersecurity.…”

Section: Related Workmentioning

confidence: 99%

Threat Assessment and Risk Analysis (TARA) for Interoperable Medical Devices in the Operating Room Inspired by the Automotive Industry

2023

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Prevailing trends in the automotive and medical device industry, such as life cycle overarching configurability, connectivity, and automation, require an adaption of development processes, especially regarding the security and safety thereof. The changing requirements imply that interfaces are more exposed to the outside world, making them more vulnerable to cyberattacks or data leaks. Consequently, not only do development processes need to be revised but also cybersecurity countermeasures and a focus on safety, as well as privacy, have become vital. While vehicles are especially exposed to cybersecurity and safety risks, the medical devices industry faces similar issues. In the automotive industry, proposals and draft regulations exist for security-related risk assessment processes. The medical device industry, which has less experience in these topics and is more heterogeneous, may benefit from drawing inspiration from these efforts. We examined and compared current standards, processes, and methods in both the automotive and medical industries. Based on the requirements regarding safety and security for risk analysis in the medical device industry, we propose the adoption of methods already established in the automotive industry. Furthermore, we present an example based on an interoperable Operating Room table (OR table).

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Section: Related Workmentioning

confidence: 99%