Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices

TL;DR

This paper highlights cybersecurity risks in AI/ML-enabled medical devices, analyzing threat data and proposing tools for pre-market risk assessment to enhance safety and security by design.

Contribution

It introduces a set of tools and techniques for security analysts to perform comprehensive premarket risk assessments of AI/ML medical devices.

Findings

Analysis of device recalls and vulnerabilities reveals key threat areas.

Proposed tools assist in early cybersecurity risk identification.

Embedding security in design improves device safety.

Abstract

The integration of AI/ML into medical devices is rapidly transforming healthcare by enhancing diagnostic and treatment facilities. However, this advancement also introduces serious cybersecurity risks due to the use of complex and often opaque models, extensive interconnectivity, interoperability with third-party peripheral devices, Internet connectivity, and vulnerabilities in the underlying technologies. These factors contribute to a broad attack surface and make threat prevention, detection, and mitigation challenging. Given the highly safety-critical nature of these devices, a cyberattack on these devices can cause the ML models to mispredict, thereby posing significant safety risks to patients. Therefore, ensuring the security of these devices from the time of design is essential. This paper underscores the urgency of addressing the cybersecurity challenges in ML-enabled medical devices at the pre-market phase. We begin by analyzing publicly available data on device recalls and adverse events, and known vulnerabilities, to understand the threat landscape of AI/ML-enabled medical devices and their repercussions on patient safety. Building on this analysis, we introduce a suite of tools and techniques designed by us to assist security analysts in conducting comprehensive premarket risk assessments. Our work aims to empower manufacturers to embed cybersecurity as a core design principle in AI/ML-enabled medical devices, thereby making them safe for patients.