Robustness of AI Models in the Healthcare Industry 

Beyond anonymity: protecting personal data in the age of AI

Artificial Intelligence is rapidly becoming embedded in healthcare operations, research, and digital patient engagement. Large Language Models (LLMs) in particular are reshaping how clinical data is processed and interpreted. 
 
At the same time, a foundational assumption is beginning to erode: that anonymization alone is sufficient to protect personal data. 
 
As of 2024, data once considered anonymous can, under certain conditions, be re-identified using advanced AI inference techniques. This development signals a structural shift in how privacy risk must be understood and governed. 

With average breach costs in healthcare reaching $10.93 million per incident, the question is no longer whether AI increases efficiency, but whether existing privacy safeguards remain adequate in an AI-driven environment 

The New Data Privacy Reality

Healthcare organizations must now confront the reality that data once considered “anonymous” may no longer meet legal and ethical standards due to AI’s ability to reverse anonymization. When automated and fueled by AI’s capacity to process vast amounts of data, this phenomenon can lead to violations of established security frameworks. One of the most relevant references in this context is the NIST AI Risk Management Framework (AI RMF)¹, which highlights the evolving privacy risks associated with AI. It explicitly encourages organizations to identify and manage risks to individuals’ privacy, including those related to re-identification and reverse anonymization, as AI technologies continue to advance

Understanding the risks

The consequences of attacks performed on LLMs are not theoretical. AI can correlate patterns in anonymized datasets with publicly available information, inadvertently exposing patient identities. Notably, research conducted by Cornell University revealed that 9% of de-identified clinical notes could be re-identified using LLMs², and their information used without consent. Moreover, the manipulation of clinical workflows and medical devices through AI misuse poses serious threats to patient safety. 

New regulations

Regulatory scrutiny is intensifying. Authorities now require demonstrable compliance. Unauthorized use of data, especially when it involves copyrighted materials containing personal information, can lead to violations of both privacy and intellectual property laws. Despite these developments, healthcare providers still operate under the assumption that anonymization offers sufficient protection. This belief underpins compliance with data privacy regulations such as the Federal Act on Data Protection (FADP), the General Data Protection Regulation (GDPR), and the Health Insurance Portability and Accountability Act (HIPAA). The consequences are significant: penalties can reach up to €20 million or 4% of global annual turnover, leading also to reputational damage and legal exposure. 

The cost of inaction

In 2024, the Change Healthcare breach caused the exfiltration of 190 million patient records via ransomware, including contact details, and medical histories³. A $22 million ransom was paid, yet the threat actor (BlackCat) retained the data, causing irreparable harm to trust and compliance. This incident exemplifies a rising trend in cybercrime, where healthcare remains the most targeted and costly sector for data breaches, with average losses of $10.93 million per incident according to IBM’s 2023 cost of a data breach report, more than twice the costs of financial institutions which rank on number 2⁴. 

Attacks on LLMs

Historically, anonymization involved the removal of direct identifiers such as names, addresses, and contact details. This approach was once considered sufficient to protect individual privacy. Today, however, the landscape has changed. Sophisticated AI systems can analyze anonymized datasets and, by drawing on contextual clues and external knowledge sources, re-identify individuals with alarming accuracy. 
In the following sections, we are going to examine two common types of attacks. 

Membership inference attack

A membership inference attack (MIA) is a privacy attack where an adversary tries to find out if a specific piece of data was used to train a machine learning model, such as a large language model (LLM). 

The attacker submits data samples such as sentences, emails, or records to the model and looks closely at the model’s responses. If the LLM produces highly accurate, detailed, or even verbatim outputs based on the input, this can indicate the input data was part of the training set. If the model only gives a generic or unrelated response, it is less likely the data was used during training. This process lets the attacker infer whether certain information is present in the training data. 

Real-world risk

For example, an attacker might submit segments of anonymized medical or purchase data to the model. By comparing the model’s responses with public information, they could potentially re-identify individuals or confirm that private records were used to train the model. This undermines traditional privacy protections and poses serious data security challenges, especially in sensitive fields like healthcare. 

LLM prompt injection attack

Prompt injection is a type of attack that targets applications powered by Large Language Models (LLMs), such as chatbots or AI assistants. 

In this attack, hackers disguise malicious instructions inside what appears to be normal user input. The goal is to trick the AI into leaking sensitive data or spreading misinformation. 

A key reason for this work is that both system instructions (the rules the developer sets) and user inputs (what people type) are combined as plain text prompts. The AI model cannot reliably distinguish between what is a developer-set instruction and what is user-supplied text. 

If an attacker carefully crafts their input, they can insert commands that override the original system instructions.  

Applying AI robustness testing in practice

Organizations that treat AI-enabled privacy risk as a strategic issue increasingly move beyond static compliance checks toward structured robustness testing. The objective is not only to confirm regulatory alignment, but to evaluate how AI systems behave under adversarial and inference-driven stress conditions. 
 
Robustness testing combines technical experimentation with governance assessment. On a technical level, adversarial simulations are conducted to determine whether models expose latent training data, infer sensitive attributes, or reveal memorized information through interaction patterns. This includes controlled membership inference testing, attribute inference analysis, and targeted data extraction attempts. 
 
These simulations are not designed to replicate isolated attack scenarios alone. Rather, they assess systemic exposure: How resilient is the dataset? How predictable is model behavior? Where do inference pathways create unintended transparency? 
 
In healthcare environments, this evaluation must extend beyond model logic to infrastructure and deployment architecture. Cloud-based AI implementations introduce additional exposure vectors, including cross-border data transfer risks, insufficient contractual safeguards with third-party providers, unclear data localization controls, and misconfigured access governance. 
 
This structured approach can be understood as an AI Robustness Maturity Assessment, a systematic evaluation of how resilient AI systems are across data, model, application, and governance layers. 
 
Such an assessment typically integrates: 

Rather than focusing on isolated vulnerabilities, an AI Robustness Maturity Assessment provides leadership with a structured view of systemic exposure and governance readiness. 
 
In an AI-driven healthcare environment, robustness is not a technical add-on. It is a prerequisite for sustainable digital trust. 
 
Such an assessment typically integrates: 

Executive AI robustness exchange

Healthcare leaders who wish to explore how inference testing and adversarial risk simulations apply to their organization may request a structured executive-level discussion with our AI governance specialists. 

References

¹ National Institute of Standards and Technology (2023). AI Risk Management Framework (AI RMF)
² Morris, J. X. et al. (2025). Adversarial Patient Reidentification with Large Language Models
³ American Hospital Association (2025). Reports: Change Healthcare cyberattack exposed data of 190 million people
⁴ IBM (n.d.). Cost of a Data Breach in the Healthcare Industry