Data Anonymization
Strip identity out of old records without wrecking your reporting.
What it adds
An irreversible scrub of identifying fields across the schema that keeps rows, relationships, and aggregate counts intact.
What your agent is told to do
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What your agent is told to do
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Classify every column as identifying, quasi-identifying, or non-identifying. Free-text notes and file names carry identity too and are the ones teams forget.
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Replace identifying values in place rather than deleting rows, so foreign keys stay valid and historical counts do not change.
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Make the replacement irreversible: random or tokenized values with no stored mapping back to the original. If a lookup table exists, this is pseudonymization, not anonymization — call it that.
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Extend the scrub beyond the primary tables to logs, search indexes, denormalized copies, cached aggregates, uploaded file contents and names, and analytics platforms.
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Record that a given record was anonymized, when, and under which policy, without retaining the identity that was removed.
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Do NOT use a deterministic transform such as hashing an email. The input space is small enough to enumerate, so the original is recoverable and the record is not anonymous.
Edge cases it handles
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Edge cases it handles
7- Quasi-identifiers combine: a rare job title plus a postcode plus a signup date can identify one person even with the name removed.
- Uniqueness constraints will collide when several anonymized rows get the same placeholder — generate unique values or relax the constraint deliberately.
- Anonymizing a user who authored content must not break the content's display; render a stable neutral label rather than a blank byline.
- Aggregate reports must produce the same totals after anonymization as before — verify this, do not assume it.
- Anonymization is irreversible, so it needs the same dry-run and confirmation discipline as deletion.
- The scrub job must be resumable and must not leave a record half-anonymized across tables.
- Backups and prior exports still contain the original identity — state that explicitly rather than claiming the data is gone everywhere.
Definition of done
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Definition of done
9- Every column is classified and every identifying column has a defined treatment.
- Records are scrubbed in place; row counts and aggregate totals are unchanged.
- Replacements are non-deterministic and no reversal mapping is retained.
- Logs, search indexes, denormalized fields, files, and third-party platforms are covered.
- An audit record proves anonymization occurred without storing the removed identity.
- Content authored by an anonymized user still renders correctly.
- The job is resumable and never leaves a record partially anonymized.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Age Verification Gate
Age Verification Gate
Ask visitors to confirm their age before restricted content, and remember the answer.
What it does
An interstitial on age-restricted pages that records a confirmation and does not ask again on every visit.
How it works
- 1 Apply the gate only to the pages that genuinely need it. Gating an entire site, including its help and legal pages, drives away visitors who were never going to see the restricted content.
- 2 Store the decision the same way the app already stores Consent and Cookie Preferences, and put the age confirmation in that same preference record rather than inventing a second cookie with its own expiry rules.
- 3 Give a refusal a real destination: an explanation page, or the site the visitor most likely came from. A blank screen or an infinite redirect loop reads as a broken site, not a policy.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/age-verification-gate
Consent and Cookie Preferences
Consent and Cookie Preferences
Let users decide about optional tracking before the scripts load.
What it does
A consent layer that blocks non-essential scripts and storage until the user allows them, and records the choice as proof.
How it works
- 1 Inventory every script, pixel, embed, and cookie the app sets, and classify each as strictly necessary, functional, analytics, or marketing.
- 2 Block non-essential categories from loading at all until consent exists. Loading a tracker and then asking is not consent.
- 3 Store the choice with a version, timestamp, and how it was given, so a later policy change can re-prompt only the people it affects.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/consent-and-cookie-preferences
Personal Data Export
Personal Data Export
Give a user everything you hold about them, safely.
What it does
A subject-access-request flow that gathers one person's data from every table and file store, packages it, and delivers it to a verified requester.
How it works
- 1 Enumerate every place personal data lives for a single user — profile, content, comments, activity, sessions, billing, support messages, uploaded files — and assemble the export from that list, not from whatever a list view happens to show.
- 2 Verify identity before generating and again before releasing the file. Re-authenticate, and require the account's second factor if one is enabled.
- 3 Generate asynchronously and notify the user when it is ready. A complete export can take minutes and must not run in a web request.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/personal-data-export
How it works
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Copy the link
Grab the Markdown instruction URL for this feature.
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Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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It inspects, then implements
Your agent reads your existing app first, then adds the feature to fit it.
Works with your stack
These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.
Need it tighter than that? Customize the feature and tell it exactly what you're running.