Guide

AI automation and data protection: what GDPR requires

As soon as AI automation processes personal data, clear obligations apply. What those are specifically, what's almost always missing in practice, and what actually suffices.

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Quote on request4 to 8 weeksAs of 2026

In short

As soon as AI automation processes personal data, you need a legal basis, purpose limitation, defined deletion periods, notification of data subjects, and a data processing agreement with every involved service provider. In practice, the same three items are almost always missing: the deletion period, the notice at the point of collection, and clarification of whether the provider uses the data for its own training.

01 — Data Protection

What GDPR requires

As soon as personal data is processed — and that happens faster than expected, for instance as soon as someone enters a name or phone number — the usual obligations apply: name the legal basis, define the purpose, maintain data minimization, define retention periods, inform data subjects.

In practice, that means for AI automation: a data processing agreement with every involved service provider, checking where processing takes place, a documented deletion period, and clear language in the privacy policy. Three things are almost always missing in practice: the defined deletion period, the notice at the point where data is collected, and clarification of whether the provider uses the data for its own training.

A data protection impact assessment is only required when there's likely high risk — not for most projects of this type. The review itself should still be documented so that if questions arise, it's clear why it was omitted.

02 — Contract

What needs to be clarified in writing

Ownership of data and access. They belong to the client. If a provider won't clearly confirm this, that's the most important information from the conversation.
Handover upon termination. In what format, within what timeframe, at what cost.
No minimum term without consideration. Lock-in is justifiable if something is delivered in return — otherwise not.
What a later change costs. The total cost isn't determined by the project price, but by the hourly rate in year three.
Documentation as a deliverable. Not as a promise, but as a line item in the proposal.

03 — Risks

What can go wrong

The system answers incorrectly. Containable by having it work exclusively from verified content and escalate when uncertain rather than guess. This is a configuration decision, not a property of the technology.
The effort is underestimated. Most common reason: unclear starting situation. An honest assessment of the current state before the proposal prevents this more reliably than any buffer.
No one maintains it. The biggest risk of all — and the most easily avoidable.
Data protection gaps. Almost always arise from missing deletion deadlines and unclear responsibility, rarely from the technology itself.
Vendor lock-in. Avoidable if access credentials, data, and documentation contractually remain with the client.

04 — Questions

Frequently asked questions

What does the EU AI Act say about this?

Most business use cases fall into the low-risk category and primarily trigger transparency obligations. Where customers interact directly with the system or AI-generated content is published, we label it. For applications in HR, we look more closely — stricter rules apply there.

How does this differ from a chatbot?

A chatbot answers questions in dialogue. AI automation runs in the background without conversation — it reads, categorizes and pre-decides while someone else is focused on the actual work.

Do we need large amounts of data for this?

No. Unlike traditional machine learning, a few dozen good examples are enough for most use cases, because the language model already brings language competence.

What if the documents or formats change?

As long as the content meaning stays the same, the system handles it well — unlike rigid, field-based recognition. Fundamentally new document types need brief adjustment, not a rebuild.

What's the difference from regular automation?

Regular automation follows fixed rules: if field A is filled, do B. AI automation comes into play where there are no fixed fields — free text, images, changing document formats. The right answer is usually both together, not one or the other.

Can we start small?

That's the only sensible approach. One use case, eight weeks, measurable outcome. What comes next, you decide based on what you've seen.

Does our data end up in a language model's training?

No. We exclusively use access where training with customer data is contractually excluded, and we process in the EU. For particularly sensitive data, we use models that run on your own infrastructure.

05 — Next

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