Data retention and deletion App Review checklist

List every data class

Inventory account records, profile data, uploaded files, purchases, subscriptions, support tickets, analytics events, crash logs, notification tokens, identifiers, location, health data, messages, and generated outputs.

For each class, record collection source, storage system, processor, retention period, deletion method, backup behavior, and legal exception.

Compare retention to user promises

• Privacy policy, App Store privacy labels, account deletion screen, support macros, and in-app settings.
• Immediate deletion, scheduled deletion, anonymization, legal hold, fraud retention, and purchase records.
• Third-party processors and exports that need separate deletion or retention handling.
• User-visible status after deletion request and after completion.
• Recovery windows that should be disclosed before destructive action.

Make retention reviewable

A reviewer should be able to find account deletion, privacy settings, and support routes without guessing. If some data is retained for legal or security reasons, explain it in policy and user-facing copy.

AppReviewReady interpretation: deletion UX should not promise more than backend systems can execute. Overpromising is worse than a clear, narrow retention rule.

Audit retention after changes

1. Recheck retention after adding SDKs, analytics events, support vendors, or payment flows.
2. Test deletion with active subscription, failed payment, open support ticket, uploaded file, and shared content.
3. Verify backups and data warehouses follow documented rules.
4. Keep support from manually restoring deleted accounts without policy basis.
5. Record deletion completion and exceptions without exposing sensitive details.

Retention ledger

The ledger connects privacy, engineering, support, and review readiness. It also reduces the risk that one team changes analytics or support storage without updating policy.

After launch, sample deletion and retention workflows. A privacy promise is operational only if it keeps working after migrations, vendor changes, and support escalations.

If the app uses AI processing, record whether prompts, uploaded files, generated outputs, and evaluation logs have separate retention rules. Users often assume generated workflows disappear when the visible project is deleted.

For support and fraud systems, define what remains after account deletion and why. A vague exception can undermine trust, while a precise security or legal retention rule is easier to explain.

When data is shared across app extensions, widgets, or web dashboards, deletion should cover every surface. Removing the main account while leaving cached extension data creates both privacy and review risk.

For enterprise customers, separate admin retention from end-user retention. A workspace owner may need audit records while an individual user expects profile deletion.

Retention ledger:
Data class: [name]
System/processor: [location]
Retention period: [time]
Deletion method: [delete/anonymize]
Exception: [legal/fraud/etc.]
User-facing promise: [copy]
Owner: [team]