What is data minimization?

Data minimization is the GDPR Article 5(1)(c) principle that personal data must be adequate, relevant, and limited to what is necessary for the purpose. In prompting it means you should not paste names, emails, or IDs into an AI system unless the task genuinely needs them.

Does this tool send my prompt anywhere?

No. All detection and rewriting happens locally in your browser using pattern matching. Nothing is uploaded to a server or to any AI provider.

How does it decide what is unnecessary?

It detects common personal data patterns — emails, phone numbers, names, addresses, card and ID numbers — then applies heuristics about which are rarely required for analysis or drafting tasks, suggesting removal or generalisation to a placeholder.

Is the rewritten prompt legally compliant?

It is a strong starting point, not legal advice. The tool reduces obvious over-collection, but you remain responsible for confirming the minimal data set actually fits your lawful basis and purpose.

Why minimize data before using AI tools?

Less personal data in a prompt means smaller breach impact, easier compliance, and lower risk of training-data retention. It is the cheapest privacy control you can apply — simply not sending what you do not need.

Data Minimization Prompt Advisor

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.