Guide

Data protection and AI compliance: what alternatives exist

Not every problem needs AI compliance. What simpler paths exist, where ready-made standard solutions are sufficient, and when the right answer is: do nothing at all.

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In short

Before AI compliance comes into question, it's worth looking at simpler paths: a more clearly written page, a different phone rule, a streamlined process. Ready-made standard solutions cover part of it and reach their limits where your own data and integrations come into play. Doing nothing is also a legitimate answer.

01 — Alternatives

What else is out there

Before AI compliance comes into question, it's worth looking at simpler paths. Sometimes a clearly written page, a different phone rule, or a streamlined process solves the same problem without any system at all.

Ready-made standard solutions cover part of the use cases and are quickly available. Their limit lies where your own processes, your own data, or integrations come into play — and in the question of who ultimately owns the data.

The third option is to do nothing. That's a legitimate answer when the process in question happens rarely or when the real bottleneck lies elsewhere. We'll say so if that's the case.

02 — Boundaries

When we advise against it

The process is still changing. As long as you're adjusting the process itself, manual work is more flexible and cheaper.
Exceptions outweigh standard cases. Then you're automating exceptions, and that gets expensive.
There's no data foundation. Without digitally available source data, the basis is missing.
Volume is too low. With five transactions a month, no system pays off — not even an affordable one.
The real bottleneck lies elsewhere. If you don't have enough inquiries, you don't need faster processing.

03 — Criteria

How quotes differ

What exactly is included. Two quotes with the same total can cover completely different deliverables. Ask for scope as a list, not as running text.
Who delivers what. Content, images, data, access — if it's not specified, it becomes a point of conflict later.
What operation costs. Calculated over three years, including licenses, hosting, and support.
What happens if you want to switch. Do you get access, data, and documentation? The answer to this question says more about a provider than any reference.
Who actually does the work. With small providers this matters: who sits in the meeting, and who then does it.

04 — Questions

Frequently asked questions

Do we need a data protection impact assessment?

Not generally. It becomes necessary when the processing is likely to pose a high risk to data subjects — such as extensive profiling. For an FAQ chatbot on a website, it's usually not required. We review this case by case and document the result in writing.

What does the EU AI Act specifically require of us?

For most businesses, it comes down to two things: transparency and competence. Transparency means people must recognize when they're speaking with an AI system or seeing AI-generated content — this has been in effect since August 2026. Competence means the people deploying such systems are adequately trained to do so. Both are documentable and achievable with manageable effort.

Do AI-generated texts need to be labeled?

For content that is published and synthetically generated or substantially modified, yes. Purely internal use — research, drafts, summaries — typically does not trigger this obligation.

What if we use multiple AI tools from different providers?

Each tool is assessed individually — provider, data processing, and risk class differ. The usage policy summarizes the results so no one has to look up each tool separately.

Does this also apply to sole proprietorships?

Yes. The AI Act doesn't distinguish by company size, but by purpose and risk. Anyone with a chatbot on their website is affected — regardless of whether one or a hundred people work in the business.

May we enter customer data into ChatGPT?

In the free version without contractual safeguards: no. With a business account, data processing agreement, and training excluded, it may be permissible — depending on what data is involved. Precisely this distinction belongs in the usage policy, because it's constantly relevant in daily operations.

How often does the inventory need to be updated?

Whenever a new tool is introduced or the purpose of an existing one changes. An annual review as a minimum interval makes sense, even without a specific trigger.

05 — Next

Data protection and AI compliance by region

Next step

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