Assessing Security Risks of AI/ML-Enabled Connected Healthcare Systems

Core Concepts

Vulnerabilities in peripheral devices connected to AI/ML-enabled medical systems can enable adversaries to manipulate data inputs to the ML engine, leading to life-threatening consequences for patients.

Abstract

The paper presents a systematic analysis of the security risks in AI/ML-enabled connected healthcare systems. It first conducts a cross-domain study of FDA-approved ML-enabled medical devices to understand the ML techniques used and the potential damage caused by mispredictions. The paper then demonstrates a case study on an ML-enabled blood glucose management system (BGMS), where an adversary can exploit vulnerabilities in the connected peripheral devices to inject adversarial data points into the ML engine, causing it to make incorrect insulin dose predictions. The paper further evaluates the state-of-the-art risk assessment techniques used by manufacturers and finds them inadequate in identifying and assessing the severity of these new security risks. The key insights are: Vulnerabilities in peripheral devices can enable adversaries to manipulate data inputs to the ML engine, leading to life-threatening consequences for patients. Existing risk assessment techniques focus on individual components rather than the end-to-end connected system, missing the security risks arising from the interplay of vulnerabilities across different components. There is a need for novel risk analysis methods that can systematically identify and assess the security risks in AI/ML-enabled connected healthcare systems, considering the diverse set of peripheral devices and communication channels.

Stats

The paper does not provide specific numerical data or metrics. It focuses on qualitative analysis and case studies.

Quotes

"We show that the use of ML in medical systems, particularly connected systems that involve interfacing the ML engine with multiple peripheral devices, has security risks that might cause life-threatening damage to a patient's health in case of adversarial interventions." "These new risks arise due to security vulnerabilities in the peripheral devices and communication channels." "Our study highlights the need for novel risk analysis methods for analyzing the security of AI-enabled connected health devices."

Key Insights Distilled From

Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems

by Mohammed Eln... at arxiv.org 04-15-2024

https://arxiv.org/pdf/2401.17136.pdf

Deeper Inquiries

How can the risk assessment process be automated to handle the large number of FDA-approved ML-enabled medical devices

Automating the risk assessment process for the large number of FDA-approved ML-enabled medical devices can be achieved through the development of AI-driven risk assessment tools. These tools can utilize machine learning algorithms to analyze the security vulnerabilities of connected healthcare systems. By training the AI model on a diverse dataset of known vulnerabilities and attack patterns, it can automatically assess the risks associated with each device. The AI system can continuously monitor for new vulnerabilities and adapt its risk assessment process accordingly. Additionally, integrating automation tools with existing risk assessment frameworks can streamline the process and ensure comprehensive coverage of all FDA-approved devices.

What are the potential legal and ethical implications of security breaches in AI/ML-enabled connected healthcare systems

Security breaches in AI/ML-enabled connected healthcare systems can have significant legal and ethical implications. From a legal standpoint, breaches that compromise patient data or lead to incorrect diagnoses or treatments can result in lawsuits against the healthcare providers, device manufacturers, and software developers. Violations of data privacy regulations such as HIPAA can lead to hefty fines and damage to the reputation of the organizations involved. Ethically, security breaches in healthcare systems can jeopardize patient safety and trust in the healthcare system. Patients rely on these systems for accurate diagnoses and treatments, and any breach that compromises the integrity of the data can have life-threatening consequences. Ensuring the security and privacy of patient data is crucial to maintain ethical standards in healthcare.

How can the security of AI/ML-enabled connected healthcare systems be improved through advancements in hardware, software, and communication protocols

Improving the security of AI/ML-enabled connected healthcare systems can be achieved through advancements in hardware, software, and communication protocols. Hardware advancements such as the implementation of secure elements and hardware-based encryption can enhance the security of medical devices. Secure boot mechanisms and tamper-resistant hardware can prevent unauthorized access to sensitive data. In terms of software, regular security updates and patches can address known vulnerabilities and protect against emerging threats. Implementing robust authentication mechanisms, access controls, and encryption protocols can safeguard data transmission and storage. Furthermore, adopting secure communication protocols such as TLS/SSL can ensure the confidentiality and integrity of data exchanged between devices and servers. By integrating these advancements into the design and development of connected healthcare systems, the overall security posture can be significantly improved.