Securing AI and Agentic Systems in Medical Devices: Methods, Risks, and Defenses

Abstract

Integrating AI and agentic systems into medical devices significantly enhances healthcare by enabling autonomous diagnosis, personalized treatment, and real-time monitoring. However, this advancement introduces complex security challenges, including risks related to autonomous decision-making, multi-agent coordination, and AI-specific attacks that can jeopardize patient safety. This whitepaper outlines a framework for securing AI-enabled medical devices, focusing on critical vulnerabilities and risk prioritization, while proposing multi-layered defense strategies based on established cybersecurity principles. It systematically addresses security risks throughout the AI system lifecycle using FDA guidance, EU regulations like GDPR and the AI Act, and the MITRE ATLAS framework. The paper concludes with a case study on implementing this framework in an AWS healthcare AI system, showcasing its practical application.

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