How to Stay GDPR-Compliant When Using AI

Does GDPR apply to AI, and how do you comply?

GDPR applies fully to AI whenever you process personal data, and complying means doing the same things GDPR always required — having a lawful basis, minimising data, being transparent, and honouring people's rights — while accounting for how AI tools handle that data. There is no AI carve-out. If a model sees names, emails, or any information about identifiable people, you are processing personal data and the regulation is engaged.

This is not legal advice, but the principles are clear enough to design around. The trouble usually comes from treating an AI vendor as a black box and forgetting that, to a regulator, you remain responsible for what happens to the data you sent. The accountability does not transfer with the data.

Establish a lawful basis and minimise

Before personal data touches a model, you need a lawful basis for processing it — commonly legitimate interest or consent, depending on context. Then apply data minimisation, which GDPR requires anyway: send only the personal data the task genuinely needs, and redact or pseudonymise the rest. Feeding a model an entire customer record to draft a short reply is both a privacy risk and a minimisation failure.

• Document why you are processing the data and under which lawful basis.
• Strip identifiers that are not needed for the task before the prompt is sent.
• Avoid sending special category data — health, biometrics, beliefs — to general AI tools without specific safeguards.

Minimisation is doing double duty here: it satisfies a GDPR principle and it shrinks the amount of personal data exposed to a third party, which is good practice regardless of the regulation.

Get the vendor relationship right

When you send personal data to an AI provider, that provider is typically a processor acting on your behalf, which means you need a data processing agreement and confidence in their terms. Check whether they train on your data, how long they retain it, and where it is processed — international transfers carry their own GDPR requirements. Many providers offer business tiers with no-training commitments and EU data residency specifically to make this workable; the consumer tiers often are not suitable for personal data at all.

To a regulator, "the AI did it" is not a defence. You chose the tool, you sent the data, and you are accountable for both.

Respect transparency and data subject rights

People have the right to know their data is being processed and, in many cases, to access it, correct it, or have it erased. AI complicates this, so plan for it. Update privacy notices to mention AI processing where relevant. Make sure you can locate and delete an individual's personal data — including in logs, caches, and vector stores — when a valid erasure request arrives. Be especially careful with automated decisions that significantly affect people, because GDPR grants additional rights, including human review, around solely automated decision-making. A model that decides something important about a person on its own is a category that deserves extra scrutiny.

Document with a DPIA where needed

For higher-risk processing — large-scale personal data, sensitive categories, or systematic profiling — GDPR expects a Data Protection Impact Assessment. Even when not strictly required, writing down the data flows, risks, and mitigations is the single best way to demonstrate accountability. If a regulator or customer ever asks how your AI handles personal data, a clear DPIA turns a stressful scramble into a short, confident answer, and it forces you to actually understand your own data flows in the process.

Prefer it handled for you?

Mapping personal data flows through your AI stack, getting vendor agreements right, and building erasure that actually reaches your logs is detailed work. talk to BSH Technologies and let our cybersecurity services help you design AI processing that holds up to scrutiny.