Exploitation of AI in the Healthcare Industry: Threats and Risk Management

The integration of artificial intelligence (AI) tools in healthcare is multifaceted, encompassing a wide array of applications and technologies. AI tools are revolutionizing the way the healthcare industry works, communicates, and solves problems. From accelerating decision-making with data-driven insights to generating sophisticated content and code in seconds, AI’s benefits are only limited by our imagination. With the ability to reduce errors, eliminate repetitive tasks, and provide around-the-clock support, AI is being heralded as a transformative force in healthcare.

AI in the healthcare industry has been widely adopted for use in diagnostics, triage, scheduling, and resource allocation. Yet, in the midst of this technological renaissance, a more sinister reality is emerging. Like any powerful tool, AI can be misused, and cybercriminals are already finding ways to weaponize it.

As healthcare organizations are embracing AI for innovation and efficiency, malicious actors are leveraging the same technology to steal and resell proprietary diagnostic models, expose vulnerable information with adversarial inputs (e.g., subtly altering MRI images to cause false diagnoses), and reconstruct sensitive patient data. Cybercriminals have developed sophisticated methods to exploit AI systems in healthcare, posing significant threats to data security and patient safety.

Here are some of the most pressing concerns regarding how AI is empowering cybercriminals and endangering digital security in the healthcare sector:

External Threats

1. Phishing, Social Engineering, and Ransomware Attacks

   Adaptive phishing email attacks use generative AI (gen AI) to mimic internal staff, physicians, or vendors with near-perfect grammar, medical jargon, and personalized context. Voice cloning (vishing) uses gen AI to impersonate doctors or administrators in calls to staff, patients, or pharmacies. Cybercriminals also use deepfake videos for fraudulent telehealth interactions or executive impersonation. AI can optimize ransomware campaigns by choosing high-value targets (e.g., electronic health record (EHR) systems, critical imaging servers) and timing attacks for maximum disruption. Smart ransomware can evade endpoint detection by dynamically changing its code or behavior.
   
   Best Practice: While detection is difficult, be alert to subtle inconsistencies in deepfake media. Victims should consider reporting incidents to law enforcement, engaging legal counsel, and notifying platform administrators. Healthcare organizations should invest in regular cybersecurity awareness training and simulate phishing attacks to keep employees alert to evolving tactics.

2. Automated Reconnaissance and Vulnerability Exploitation

   AI tools can scan vast hospital networks for outdated healthcare devices, electronic EHR systems, or exposed application programming interfaces (APIs) much faster than human attackers. Automated exploitation can identify and attack misconfigured cloud-based patient portals or telehealth platforms in real time.
   
   Best Practice: Healthcare organizations should deploy strong endpoint protection and maintain up-to-date patches across all software and hardware to close off vulnerabilities.

3. Medical Data Theft and Manipulation

   AI-assisted attacks can exfiltrate large volumes of protected health information (PHI) while evading detection. What’s even more concerning is data poisoning, where attackers alter patient records, lab results, or imaging data to mislead diagnoses or disrupt care. Cybercriminals create synthetic identities using AI-generated patient profiles to commit insurance or billing fraud.
   
   Best Practice: To reduce the risk of AI data poisoning attacks, healthcare organizations should use trusted data sources, implement strict data validation, and monitor for anomalies that may indicate tampering. Securing data pipelines, applying access controls, and regularly retraining models with clean, verified datasets are also essential to maintaining model integrity.

End User Threats

1. Unintentional Exposure of Sensitive Data

   Users may unknowingly upload sensitive or proprietary information into AI chatbots, unaware that this data could be stored or exposed through future system vulnerabilities.
   
   Best Practice: Organizations should establish clear usage policies for AI tools, restricting access to trained personnel and enforcing safeguards for handling confidential information.
   

2. AI Chatbot Data Breaches

   Even AI platforms themselves are not immune to attacks. OpenAI, for instance, disclosed a data leak caused by a bug in its source code. Such incidents can expose not only user credentials but also the queries and data shared with the chatbot.
   
   Best Practice: Always consider the potential consequences of your input being publicly accessible. Avoid sharing any confidential, proprietary, or personally identifiable information through AI tools unless you are certain of their security protocols.

Balancing Promise with Precaution

AI offers tremendous potential for improving healthcare, but it also presents new security challenges. Cybercriminals are adept at exploiting vulnerabilities in AI systems, putting patient data and safety at risk. By implementing comprehensive cybersecurity measures, training staff, and ensuring data integrity, healthcare organizations can mitigate these risks and harness the power of AI safely. As the healthcare industry continues to evolve, staying vigilant against cyber threats is crucial in protecting both patients and providers.

For more information on the risks of artificial intelligence in healthcare and keeping your organization safe and secure, contact Kevin Ricci at kricci@citrincooperman.com.