Data minimization prompt advisor
Most prompts contain more personal data than the task requires. People paste a full customer record to ask “draft a polite reply,” when the model only needs the message body. The data minimization prompt advisor scans your prompt for personal data elements, flags the ones that are unlikely to be necessary, and produces a rewritten version that keeps only what matters — applying the GDPR Article 5(1)(c) principle of adequate, relevant, and limited to what is necessary. Everything runs locally in your browser.
How it works
The advisor uses pattern matching to detect common categories of personal data:
email addresses, phone numbers, postal addresses, payment-card-style numbers,
national ID patterns, and likely person names. Each detected element is shown
with a recommendation — keep, generalise, or remove — based on heuristics about
how often that category is genuinely needed for analysis or drafting work. It
then generates a minimized version of your prompt where unnecessary identifiers
are replaced with neutral placeholders such as [NAME] or removed entirely, so
you can copy a cleaner prompt that exposes far less personal data.
Tips and notes
- Default to placeholders. If the model needs to reference a person, a token
like
[CUSTOMER]usually works as well as a real name. - Strip identifiers you only need on your side. Account numbers and emails rarely change the model’s output — keep them out of the prompt.
- Pattern matching is conservative. Review every flag; unusual formats or names the tool misses are still your responsibility to redact.
- Minimization is a control, not a cure. Pair it with a no-training data setting and short retention on your provider account.
What GDPR Article 5(1)(c) actually says
The data minimization principle is part of the core GDPR principles in Article 5(1)(c). It requires that personal data be:
- adequate — sufficient to properly fulfil the stated purpose,
- relevant — pertaining to the purpose, and
- limited to what is necessary — not more than is required for that purpose.
The phrase “limited to what is necessary” is the key clause. Applied to AI prompting, it means you should not include a customer’s name in a prompt if the task only requires the content of their message. You should not include an email address if the model never needs to address a reply. You should not include an account number if it plays no role in what you are asking the model to do.
The AI-specific risk: training and retention
When you send personal data to an external AI provider, you typically lose direct control over it. Different providers have different data retention and training policies:
- Some providers use API inputs to improve their models by default (often with an opt-out).
- Most retain inputs for safety monitoring for some period.
- Logged prompts can be accessed by provider staff under defined conditions.
Data minimization reduces exposure on all of these axes simultaneously. If a name was never in the prompt, it cannot be retained, logged, or used for training.
Practical categories and whether they are usually necessary
| Data type | Usually necessary? | Safer alternative |
|---|---|---|
| Full name | Rarely | [CUSTOMER] or [NAME] |
| Email address | Almost never | omit entirely |
| Phone number | Almost never | omit entirely |
| Physical address | Rarely (exception: mapping/distance tasks) | omit or generalise to city |
| Account / ID number | Rarely | internal reference, not in prompt |
| Date of birth | Only if age-calculation is the task | [AGE: 34] instead |
| Medical detail | Only if clinical summary is the task | discuss with DPO |
| Message content | Often yes | keep, it is the subject of the task |
The pattern is consistent: identifiers that link back to a person (name, email, phone, ID) are almost never required for the model to do its job, while the content that describes the situation usually is. Strip the former; keep the latter.