# Data Retention Controls

## Objective

Delete data you no longer need, on a schedule, instead of keeping everything forever.

Configurable retention windows per data type, with a scheduled job that deletes expired records and everything derived from them.

## Before You Begin

This feature is being added to an application that already exists and already
works. Do not scaffold a new project, and do not assume a blank slate.

Inspect the codebase first and establish:

- The existing application structure and where code of this kind already lives.
- The framework and version in use.
- The existing design system — colours, spacing, typography, and component conventions.
- Existing UI components you can reuse instead of writing new ones.
- The existing database structure, if this feature needs to persist anything.
- The existing authentication and authorization system, if this feature is user-scoped.
- Dependencies already installed, so you don't add a library that duplicates one.
- The existing test setup and conventions.

Only start writing code once you understand the above. If the application
already implements part of this feature, extend it rather than replacing it.

## Implementation Instructions

1. Build a data map first: every table, file store, log stream, search index, cache, and third-party processor that holds customer data, and how old the oldest row is.
2. Define a retention window per data type, not one window for the whole app. Activity logs, uploaded files, and billing records have different obligations.
3. Run deletion as a scheduled, idempotent, batched job that can be stopped and resumed, and that records what it deleted in an audit trail.
4. Support legal holds: a flag that exempts a record, workspace, or user from deletion until it is cleared, and that survives the job being re-run.
5. Delete derived copies at the same time — search index documents, thumbnails, exports, cached aggregates, and data pushed to analytics or support tools.
6. Do NOT delete without a dry-run mode and a grace period. A retention job with an off-by-one window can destroy years of data in one run, and there is no undo.

## UI and UX Requirements

Match the application's existing design system exactly. Reuse its components,
spacing, and typography. This feature should look like it was always there.

## Responsive Requirements

Works on mobile, tablet, and desktop. Touch targets are large enough to hit on a
phone, and nothing overflows horizontally at 320px.

## Accessibility Requirements

- Fully keyboard navigable.
- Correct semantic elements and ARIA roles.
- Visible focus states.
- Meets WCAG AA contrast.
- Dynamic changes are announced to screen readers.
- Respects prefers-reduced-motion.

## Edge Cases

- Backups are not covered by live deletion. State the backup retention period explicitly and accept that deleted data persists there until backups roll off.
- Deleting a parent row can orphan or cascade into children — decide per relationship, and count rows in the dry run.
- Records under legal hold must be skipped without failing the batch or silently resetting their expiry.
- Retention windows differ by plan and region; a per-workspace override must not be overwritten by the global default.
- Data already sent to third parties needs a deletion call to those APIs, and some will not have one — document that gap.
- A job that times out mid-batch must not delete a record's rows in one table and leave the rest behind.
- Changing a window from 24 months to 6 months queues a very large first deletion — throttle it rather than running it in one pass.

## Testing

Exercise the feature end to end in the running application. Cover every edge case
above, then run the existing test suite and confirm nothing regressed.

## Acceptance Criteria

- [ ] Every store of customer data appears in the data map with a stated retention window.
- [ ] Windows are configurable per data type and overridable per workspace.
- [ ] The deletion job is idempotent, batched, resumable, and auditable.
- [ ] Legal holds prevent deletion and survive repeated runs.
- [ ] Derived data, files, search indexes, and third-party copies are removed alongside the primary record.
- [ ] A dry-run reports exactly what would be deleted before anything is.
- [ ] Backup retention is documented and distinguished from live deletion.
- [ ] The feature matches the existing design system.
- [ ] No existing functionality is broken.

## Adaptation Rules

- Match the existing design system. Do not introduce a new colour palette,
  spacing scale, or component library.
- Reuse existing components and utilities wherever they fit.
- Follow the naming, file layout, and code style already present.
- Do not upgrade, replace, or remove existing dependencies to make this
  feature fit. Adapt the feature to the app, not the app to the feature.
- Do not break existing functionality. If a change is genuinely required in
  existing code, make the smallest one that works and say so.
- If something in these instructions conflicts with how the application is
  built, follow the application and explain the deviation.

## Final Verification

Before you report the work as done:

1. Re-read the acceptance criteria above and check each one against what you
   actually built.
2. Run the application and exercise the feature end to end.
3. Run the existing test suite and confirm you have broken nothing.
4. Check the feature on mobile, tablet, and desktop widths.
5. Check keyboard navigation and focus handling.
6. Summarize what changed: files added, files modified, and anything you
   deliberately did differently because of how this application is built.

If any acceptance criterion is unmet, fix it before reporting completion.