We look around, and AI is everywhere. Artificial intelligence has proved its worth for almost every sector, from retail and education to healthcare.
The UK is taking its potential for improving healthcare very seriously.
In January 2025, the Guardian reported that the UK was considering building a National Data Library to help researchers train AI models. It would also, potentially, include health data.
Recent media reports suggest that the resulting AI model was trained on millions of NHS records in an attempt to predict hospitalisation and understand illnesses. The data is immense, from outpatient visits to vaccination records and more.
While the model’s use is currently highly restricted, the apprehensions that arise are legitimate. How can the makers guarantee that it won’t disclose sensitive patient data? What will be the consequences of a massive data breach like that, especially for PII (Personally Identifiable Information) pertaining to health?
Continuous Security Monitoring of Related AI Models
The risk of a data breach increases significantly when the AI models in question have unaddressed vulnerabilities. For example, all the patient data fed to the model is de-identified to remove any underlying connection. However, such models do have an undeniable probability of re-identification.
New Scientist recommends that the NHS consider checking if the AI model could memorise data used during training. It could help understand the probability of leaking sensitive information.
Memorisation has been noticed in a few situations, where AI models generate copies of their training data. Major brands have been affected by this, like the New York Times, which filed a copyright infringement case against ChatGPT. The media house claimed that the latter was producing sections of its articles in response to prompts—a clear case of memorisation and also a copyright violation.
In any case, continuous monitoring will be non-negotiable to recognise and manage these risks. This auditing can also verify that access control is in place, restricting sensitive data only to approved researchers.
More Accurate Digital Identity Verification
The growing use of AI extends to multiple modes of delivering healthcare, like consulting a chatbot therapist or accessing telemedicine. It introduces more data into the models, thus also raising the risk of a breach.
In this scenario, checking online identity will be crucial to ensure that sensitive data is not leaked to an unauthorised person or entity. Companies can consider using a fast digital ID verification service to implement strict security measures. It can verify the authenticity of uploaded identity proofs by checking security elements such as holograms.
According to AU10TIX, more stringent security checks can involve biometric verification and AI-powered fraud detection. After all, what’s better than dealing with technology risks with superior technology?
Digital identity verification will also further compliance in healthcare, bringing peace of mind to the firm in question. With rising cyber threats, identity theft is hardly a problem healthcare can afford to take lightly.
Training Customers on Safe Data Sharing Practices
Another privacy risk in AI models that use health data comes from the users themselves, particularly people who may overshare.
A 2025 Common Sense Media report found that over half of the surveyed teenagers used AI chatbot platforms many times a month. Many of them use these companions for friendships and romantic relationships. However, these interactions may also expose personal health data, such as problems with tobacco or alcohol use.
Experts from the American Medical Association offer meaningful advice on handling this situation: encouraging family support and introducing regulatory rigour. For example, the US state of Illinois has banned the use of ChatGPT for mental health therapy.
More healthcare providers are also encouraging families to have conversations about AI chatbots. These discussions can help young adults understand the boundaries of safe data sharing.
However, the problem is not restricted to teenagers or young adults. Some reports indicate that employees often enter sensitive prompts into GenAI tools. This data enters the LLM (Large Language Model) dataset and becomes the source material for the algorithm’s ongoing training.
Businesses have started planning to mitigate these risks by strengthening their AI governance policies. It will also involve tracking inputs to AI models in real time and educating employees on responsible usage. Healthcare organisations can benefit from following a similar approach that encompasses digital literacy training programs for users and employees.
Vast realms of data now accessible to businesses and other profit-seeking entities are a validly worrisome situation. Health data is particularly sensitive since it connects with various sectors, from an individual’s insurance claims to their suitability for a job position. That said, AI tools that can predict health parameters and outcomes, such as diagnoses and hospitalisation rates, can be immensely valuable.
UK healthcare companies would do well to balance this scenario by emphasising data security and privacy to reassure the broader public. It won’t do to maintain a myopic vision about what AI can achieve, but staying guarded never harmed anyone.
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