Securing an AI that handles health data

An AI in healthcare faces risks that traditional systems do not. The four checkpoints to verify before going live.

Risks specific to AI

Prompt injection, data leakage through model outputs, training-data poisoning: these threats come on top of the usual cyber risks.

Hosting and compliance: health data hosted by an HDS-certified provider, a clear GDPR legal basis.
Minimisation: send the model only the data it needs, pseudonymised where possible.
Adversarial testing: stress-test the system before go-live (red teaming).
Traceability: log requests and decisions, with a human in the loop for sensitive cases.

AI security is designed in from the architecture (security by design), then monitored over time: a model evolves, and so do its risks.

General information, not legal advice.

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