The integration of large language models (LLMs) into health care offers tremendous opportunities to improve medical practice and patient care. Besides being susceptible to biases and threats common to all artificial intelligence systems, LLMs pose unique cybersecurity risks that must be carefully evaluated before these AI models are deployed in health care.
The cybersecurity threats posed by LLMs in health care can be categorized into three main areas
- AI-inherent vulnerabilities: like Data or model poisoning, Backdoor attacks, Prompt Injection, and Denial of Service.
- Non-AI-inherent vulnerabilities: like Remote Code Execution, Side channel attacks, and Supply chain vulnerabilities.
- Cyberattacks carried out using LLMs include hardware-level, operating system-level, software-level, network-level, and user-level attacks.
To securely implement LLMs, health care institutions must adopt a multilayered security approach, starting with first deploying models in secure,
isolated environments, ie, sandbox.
In addition, model interactions should be continuously monitored, encrypted, and controlled through authentication mechanisms such as multifactor identification and role-based access control.
To safely integrate LLMs into health care, institutions must focus on robust security measures and continuous vigilance to prevent exploitation. This includes ensuring secure deployment environments, strong encryption, and continuous monitoring of model interactions.
By implementing robust security measures and adhering to best practices during the model development, training, and deployment stages, stakeholders can help minimize these risks and protect patient privacy.
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