AI data breaches in healthcare: protecting patient privacy & trust

Projections on the potential for AI to improve patient outcomes and accessibility are positive, but they must be weighed against potential attacks on AI systems. In 2024 alone, data security breaches exposed the health information of more than 182.4 million individuals, according to the U.S. Department of Health and Human Services' Office for Civil Rights. 

As AI becomes more integrated into healthcare systems, it introduces even more risks, making patient data even more susceptible to leaks, attacks, and manipulation.

How AI puts patient privacy at risk

Here are some examples of the types of vulnerabilities and attacks AI-driven healthcare systems are exposed to that engineers and security teams should be aware of during design.

1. Data memorization

Healthcare AI models, in particular those trained on patient records, can inadvertently memorize sensitive information. During training, these models may store fragments of personal health information (PHI) in their parameters. This creates a risk where carefully crafted prompts from malicious attackers could extract sensitive details like patient names, diagnoses, or treatment plans—even from models that appear to be properly sanitized.

2. Model poisoning

When you train an AI model, you assume the data is clean and trustworthy. But what if it isn’t? Attackers can inject false or malicious data into training sets, subtly altering how your AI model behaves. Common poisoning techniques include:

• Training data poisoning: Attackers inject malicious examples that create backdoors, allowing them to manipulate the model's behavior on demand
• Model fine-tuning attacks: Bad actors compromise the fine-tuning process to introduce vulnerabilities without affecting general performance
• Label poisoning: Deliberately mislabeled training data causes the model to make predictable mistakes that can be exploited later

In healthcare, this could mean a model misdiagnoses conditions or learns to bypass fraud detection. If you’re not validating and securing your training data, your AI could become a liability instead of an asset.

3. Adversarial attacks

AI models can be tricked with specially crafted inputs, leading them to make incorrect predictions or even reveal sensitive data. Using sophisticated querying techniques, attackers probe AI systems to reconstruct sensitive patient information from the model's responses. They can even manipulate an image or data entry just enough to fool your model into misclassifying a disease or exposing private patient information.

4. Data exfiltration

Even when direct access to training data is restricted, AI models can leak patient information through their APIs. Attackers can systematically query these interfaces to piece together protected health information. This risk is particularly acute in healthcare settings where models must maintain high accuracy—the very precision that makes them useful also makes them vulnerable to inference attacks.

5. Malicious use of AI

Cybercriminals can leverage AI to create fake medical records, forge prescriptions, and automate phishing attacks against healthcare providers. Common threats include:

• AI-generated phishing: Sophisticated emails that mimic healthcare provider communication styles and patterns
• Automated vulnerability scanning: AI systems that continuously probe for security weaknesses in healthcare networks
• Social engineering enhancement: Deepfake voice and video tools that impersonate authorized personnel
• Automated patient data extraction: AI-powered tools that systematically harvest information from compromised systems

These AI-enhanced attacks are particularly effective because they can adapt to defensive measures and operate at scale.

AI data breaches in the real world

In the past five years, healthcare data breaches have escalated dramatically, with hacking and IT incidents leading the sharp increase in the amount of data maliciously accessed.

While AI has significantly advanced various industries, it has also introduced new avenues for cyberattacks. Below are notable incidents where AI facilitated data breaches that could be incredibly harmful to healthcare entities everywhere.

DeepSeek data exposure (2024)

DeepSeek, a Chinese AI platform, inadvertently exposed critical databases containing over 1 million records, including system logs, user prompts, and API tokens, due to unsecured configurations. This AI data breach exposure posed significant security risks, as sensitive data was accessible on the internet.

If a healthcare AI model suffered a similar exposure, attackers could extract sensitive patient queries, AI-assisted diagnostic results, or even reconstruct patient-provider interactions, leading to severe privacy violations and compliance breaches.

Microsoft's copilot exploitation (2023)

At the Black Hat security conference, researcher Michael Bargury demonstrated how Microsoft's Copilot AI could be manipulated to perform malicious activities, including spear-phishing and data exfiltration. By exploiting Copilot's integration with Microsoft 365, attackers could access emails, mimic writing styles, and send personalized phishing emails containing malicious links.

This kind of AI-assisted phishing attack could be used in the healthcare industry to impersonate doctors or hospital administrators, tricking staff into revealing patient records or granting access to sensitive systems.

Clearview AI data breach (2020)

Clearview AI, a company specializing in facial recognition technology, experienced a data breach that exposed its client list and internal data. The AI data breach raised significant concerns about privacy and the potential misuse of biometric data.

In a healthcare context, a similar breach could compromise patient identities, leading to unauthorized access to medical records and undermining trust in AI-driven diagnostic tools.

Protect patient privacy from an AI data breach

To reduce your AI privacy risks, focus on these key safeguards:

• ‍Synthetic data: Train robust models using statistically accurate synthetic data instead of risking real patient information.‍
• Data de-identification: Use advanced de-identification techniques designed specifically for AI training data to prevent the reconstruction of patient information.
• Monitoring: Keep your AI systems under continuous surveillance to detect potential data leakage or suspicious query patterns.

In addition to these protections, adopt automated data provisioning workflows to ensure secure, access-controlled environments for AI model development and testing.

The path forward

As AI continues to transform healthcare delivery, protecting patient privacy requires a proactive approach. By implementing robust safeguards and using advanced tools like synthetic data generation, you can harness AI's benefits while maintaining the trust of your patients and meeting regulatory requirements.

Ready to protect your AI systems with synthetic data? Request a demo to see how Tonic.ai's platforms can help secure your patient data.