AddThisFeature

Data Anonymization

Strip identity out of old records without wrecking your reporting.

involved Privacy & Compliance

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

6
  1. 1

    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.

  2. 2

    Replace identifying values in place rather than deleting rows, so foreign keys stay valid and historical counts do not change.

  3. 3

    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.

  4. 4

    Extend the scrub beyond the primary tables to logs, search indexes, denormalized copies, cached aggregates, uploaded file contents and names, and analytics platforms.

  5. 5

    Record that a given record was anonymized, when, and under which policy, without retaining the identity that was removed.

  6. 6

    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

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

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

How it works

  1. 1

    Copy the link

    Grab the Markdown instruction URL for this feature.

  2. 2

    Give it to your AI

    Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.

  3. 3

    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.