AI in Medical Devices: Managing Cybersecurity Risks

Integrating artificial intelligence (AI) into medical devices has transformed healthcare by enhancing diagnostics, patient monitoring, and automation. However, these advancements introduce cybersecurity risks that must be addressed to ensure patient safety. As AI continues to evolve—particularly with the rise of large language models (LLMs) alongside traditional machine learning (ML)—manufacturers and healthcare providers must proactively address vulnerabilities and comply with FDA regulations.

The Cybersecurity Challenges of AI in Medical Devices

AI-driven medical devices collect, process, and analyze vast amounts of patient data. These systems often rely on cloud computing, ML algorithms, and networked environments, increasing their exposure to cyber threats. The FDA’s recent AI-Enabled Device Software Functions draft guidance highlights several AI risks, including:

• Data Poisoning: Malicious actors injecting false data to manipulate medical diagnoses.

• Model Inversion/Stealing: Attackers inferring or replicating models, leading to privacy risks.

• Model Evasion: Manipulating inputs to mislead AI systems, affecting prediction reliability.

• Data Leakage: Exploitable vulnerabilities that allow unauthorized access to sensitive training data.

• Overfitting: Models trained too rigidly on specific datasets can become vulnerable to adversarial manipulation.

• Bias & Backdoors: Corrupted or biased training data can introduce systemic errors into AI decisions.

• Performance Drift: AI models can degrade over time if not continuously monitored and updated.

Emerging Risks with Large Language Models (LLMs)

Unlike traditional ML, LLMs often do not require extensive training but instead rely on prompt engineering to generate responses. This introduces new security challenges:

• Prompt Injection Attacks: Attackers crafting deceptive inputs to manipulate AI responses, potentially bypassing security measures.

• Unsecured Use by Healthcare Workers: Employees using consumer-grade LLMs (e.g., ChatGPT, Google Gemini) for automation may inadvertently expose protected health information (PHI) or proprietary data.

• Confidential Data Retention & Leakage: Some AI tools retain prompts, raising the risk of data exfiltration if sensitive queries become part of future model responses.

• LLMs in Decision-Making: If healthcare workers rely on AI-generated content for clinical decisions, errors or biases could pose significant patient safety risks.

A notable example of these risks occurred when Samsung engineers inadvertently leaked confidential trade secrets by inputting sensitive code into a public AI model. Similar risks exist in healthcare if providers unknowingly submit patient data to unsecured AI tools.

Securing AI-Driven Medical Devices in Compliance with FDA Guidelines

To mitigate cybersecurity risks, manufacturers and healthcare organizations must implement robust security measures while adhering to FDA cybersecurity guidelines. Key strategies include:

1. Risk-Based Cybersecurity Framework

• Follow FDA premarket cybersecurity guidance, emphasizing risk-based security approaches.

• Identify AI-specific threats, including LLM-based risks, data poisoning, and adversarial attacks.

• Implement security controls proportional to identified threats.

2. Secure Software Development & Lifecycle Management

• Utilize secure coding practices to minimize vulnerabilities in both ML and LLM-based systems.

• Conduct regular security testing, including adversarial ML testing and prompt injection testing.

• Ensure LLM integrations comply with privacy laws (e.g., HIPAA, GDPR) when handling medical data.

3. Data Encryption & Access Controls

• Encrypt data in transit and at rest to prevent unauthorized access.

• Implement role-based access controls (RBAC) and multi-factor authentication (MFA) to restrict system access.

• Use secure AI APIs to prevent unauthorized queries that might expose sensitive data.

4. AI Model Transparency & Robustness

• Ensure explainability in AI decision-making to detect anomalies or adversarial interference.

• Deploy robust validation mechanisms to safeguard against data poisoning or model evasion.

• Monitor AI performance continuously to detect drift or security vulnerabilities.

5. Incident Response & Vulnerability Management

• Develop an AI-specific cybersecurity incident response plan aligned with FDA recommendations.

• Establish a coordinated vulnerability disclosure (CVD) process for AI-related threats.

• Update security protocols regularly as new AI risks emerge.

6. Training & Awareness for AI Use in Healthcare

For Software Engineers Developing AI in Medical Devices

• Educate engineers on LLM security risks, including prompt injection attacks and adversarial manipulation.

• Implement privacy-preserving AI techniques to protect patient data.

• Train developers in compliance frameworks (e.g., FDA’s AI cybersecurity guidance, HIPAA, ISO 27001).

For Healthcare Workers Using AI Tools

• Provide training on responsible AI use, ensuring staff do not input PHI or proprietary information into unsecured AI models.

• Implement enterprise-grade AI solutions with governance controls to prevent accidental data exposure.

• Encourage ongoing awareness programs to educate employees on AI ethics, security, and compliance.

The Role of FDA in AI Cybersecurity Compliance

The FDA’s AI cybersecurity framework continues to evolve to address emerging threats. Some key regulatory initiatives include:

• Premarket Cybersecurity Guidance: Encouraging manufacturers to integrate security from the design phase.

• Postmarket Cybersecurity Requirements: Enforcing continuous AI monitoring and risk mitigation for deployed medical devices.

• Software Bill of Materials (SBOM): Requiring transparency in software components to track vulnerabilities, especially in LLM-based applications.

Conclusion

As AI continues to revolutionize medical devices, cybersecurity must remain a top priority for manufacturers, software engineers, and healthcare providers. While securing traditional machine learning systems is essential, the emergence of large language models (LLMs) introduces new and evolving risks. These include prompt injection attacks, unsecured AI APIs, and potential misuse of AI outputs—all of which demand immediate attention through proactive safeguards.

The healthcare industry can address these challenges by implementing FDA-compliant security frameworks, conducting rigorous AI validation, and adopting comprehensive risk management strategies. Doing so not only mitigates AI-driven cyber threats but also ensures patient safety, protects intellectual property, and maintains regulatory integrity.

At RQMIS, we specialize in helping organizations navigate this complex landscape.

Our expertise in regulatory compliance, cybersecurity, and AI risk management for medical devices enables us to deliver tailored solutions that safeguard your AI-driven innovations. A recent example of our success is our collaboration with the U.S. Army, where we provided strategic support to advance complex technology solutions while meeting stringent regulatory and security requirements.

Read how RQMIS successfully assisted the U.S. Army and discover how we can help secure your AI-powered medical technologies while ensuring full FDA compliance.