Data protection for AI in veterinary practice: a GDPR-first checklist
Practices hold sensitive data about clients and, increasingly, run it through AI tools. This is a practical, GDPR-first checklist for adopting AI without creating a data-protection problem.
Veterinary practices hold a lot of personal data. Client contact details, payment records and correspondence are all covered by the UK GDPR, and consult notes often contain personal data about the owner even though the patient is an animal. Before you run any of that through an AI tool, it is worth working through a short checklist so adoption does not create a data-protection problem.
This is general guidance, not legal advice - use it to structure the questions you put to any vendor, including us.
1. Know what data the tool actually sees
Start by mapping the flow. What does the tool receive: audio, free text, client identifiers, payment data? The less personal data a tool needs, the smaller your exposure. A well-designed clinical tool minimises what it takes in and is explicit about it.
2. Establish a lawful basis and be transparent
Identify your lawful basis for the processing and make sure your privacy notice reflects that AI tools are used. Clients should be able to understand, in plain terms, how their information is handled. Transparency is a GDPR requirement, not a nicety.
3. Ask where the data is processed and stored
Data residency matters. Ask whether processing and storage stay within the UK or the EEA, or whether data leaves for a country covered by adequacy or appropriate safeguards. "In the cloud" is not an answer. You should be able to name the regions.
4. Check for de-identification before any model call
A strong pattern is to strip or mask identifiers before sending anything to a language model, so the model never sees who the record is about. Ask whether the tool de-identifies on the way in, and what happens to identifiers it detects. This single control removes a large share of the risk.
5. Get the sub-processor list
Any serious vendor can tell you which sub-processors touch your data - the model provider, the hosting platform, transcription, storage - and where each one operates. If a vendor cannot produce that list, treat it as a red flag. You are accountable for your processors, so you need to see them.
6. Pin down retention and deletion
Ask how long audio, transcripts and derived records are kept, and how deletion works. Short, clear retention windows are better than vague ones. For anything captured transiently, such as consult audio, "deleted after the note is drafted" is a far stronger position than indefinite storage.
7. Confirm the contractual cover
You will typically need a data processing agreement with the vendor. Check it names the sub-processors, sets the retention terms and reflects the residency commitments you were given verbally. The contract is what makes the promises enforceable.
A quick reference
| Question | What good looks like |
|---|---|
| What data does it see? | The minimum needed, clearly listed |
| Where is it processed? | Named UK or EEA regions |
| Is it de-identified? | Yes, before any model call |
| Who are the sub-processors? | A published, current list |
| How long is it kept? | Short, explicit retention windows |
| Is there a DPA? | Yes, matching the above |
Working through these before you switch a tool on turns AI adoption from a leap of faith into a documented decision. If you want to see how Arlo handles de-identification, residency and retention, book a demo.