Log Retention for Local AI Apps

Direct answer

Log retention should start with a strict content boundary: ordinary operational logs describe task state and failures, not the prompt, transcript, image, model output, credentials, or private path. Android security guidance warns that sensitive data in logs can expose users and recommends sanitizing production logging. PRIVACY-01 and ANDROID-LOG-DISCLOSURE-2026-01 support minimizing diagnostic fields, but they do not specify a universal retention period or prove a particular app’s logging behavior. Inventory every log-like store, not just Logcat. Include application files, database tables, crash-report SDK buffers, analytics queues, worker output, test reports, screenshots, support bundles, and backups. For each store record its owner, field schema, creation trigger, audience, rotation or expiry, deletion path, and whether it can leave the device. Logs emitted to the platform, files written by the app, and records sent to a vendor are different data paths and may have different lifetimes. Define a small diagnostic schema that supports an operational question. It may include event category, app/build version, coarse task stage, error class, and timestamp where needed. Avoid free-form exception messages if they can contain prompt fragments or file paths. Do not assume a hash makes content anonymous; repeated hashes can link events. Record whether an identifier is stable across tasks and whether it is necessary. Keep source content in test-only fixtures and prevent production logging APIs from receiving it. Retention should be explicit per store. A short-lived in-memory buffer, a rotated file, an upload queue, a crash report retained by a service, and an exported support archive are not one policy. The appropriate duration depends on purpose and implementation; this guide does not invent a legal timeframe. Define the trigger that expires each store, whether a user reset changes it, and whether app backup restores old logs. If a vendor controls server-side retention, link its published configuration and avoid claiming that local deletion removes that copy. Build synthetic canaries for prompts, transcripts, email addresses, paths, and output text. Trigger success, exception, cancellation, background retry, process death, and storage-full conditions. Search each local and exported store after the event. Then advance the app’s supported cleanup mechanism or use a deterministic test clock to verify expiry. Test that crash paths do not stringify request objects and that an exception message is sanitized before it enters a report. Test release and debug separately, but do not let build type replace retention policy. A debug trace may have a different level of detail; it still needs synthetic data and bounded storage. A release build can also retain logs in caches or upload diagnostics. Verify opt-in controls, reset behavior, and service queues independently. If users can submit a support bundle, preview its contents and state which logs are included before transfer. The final record should name log stores, schemas, triggers, retention/expiry events, backup behavior, upload destinations, canary results, and known limits. It may report that a marker was absent from inspected stores after a tested cleanup. It cannot promise forensic erasure, complete removal from a third-party service, or a global retention duration without evidence. Re-review after changing logging libraries, crash SDKs, analytics, backup rules, or support flows. The goal is useful diagnostics with a verifiable content and lifetime boundary.

A failed inference leaves a diagnostic bundle on device

A tester triggers a model error and the app offers a support bundle containing operational logs. The team needs enough state to reproduce the failure but must ensure that a prompt canary, private file path, or generated answer does not remain in temporary files, backups, or the exported archive.

Decision

Use synthetic markers, map every log store, exercise expiry and reset, and preview the exact support archive before any user-selected transfer.

Expected outcome

The report identifies which log classes were inspected and the tested cleanup behavior without claiming forensic erasure.

Evidence to collect

The claim becomes reviewable only when the following evidence is attached to the same artifact and test run:

• Log field inventory
• Retention and deletion test
• Diagnostic export review

A privacy claim needs a data inventory and traffic evidence. A local-model label by itself is not proof.

Implementation workflow

Record and compare Log field inventory, Retention and deletion test, and Diagnostic export review to support retention work. Use these three items together, noting what each one captures, how they align, and where differences appear, so the comparison stays grounded in what is recorded.

1. Inventory log and crash stores. List Logcat, files, databases, SDK buffers, worker output, crash reports, analytics queues, backups, and support archives with an owner for each.
2. Set a content-safe diagnostic schema. Keep event categories and reviewed metadata; exclude prompts, transcripts, images, outputs, credentials, free-form errors, and private paths before production release tests.
3. Inject content canaries into task paths. Trigger success, failure, cancellation, process death, retry, and storage pressure with unique synthetic strings, then search each local store and export.
4. Verify expiry, reset, and backup behavior. Run cleanup with a deterministic clock or supported mechanism; test reset and restore to find stale log or queue copies.
5. Review export and destination behavior. Preview the support bundle, list included log classes, and verify user confirmation before transfer to any external recipient or service.

Acceptance criteria

• Every log-like store has an owner, field schema, retention trigger, deletion path, and backup or transmission status.
• Synthetic prompt, path, transcript, and output canaries are absent from restricted stores after the tested cleanup flow.
• The report avoids claims of forensic erasure or third-party deletion unless those outcomes are independently verified.

Decision table

Failure patterns to prevent

• Extending retention silently
• Recording prompts or file paths

Extending retention silently and Recording prompts or file paths are pitfalls to rephrase, limiting release wording to the recorded evidence. Refer back to PRIVACY-01 and PRIVACY-02 as the boundary identifiers for these pitfalls, and keep release statements aligned with what the evidence actually shows.

Minimum reproducibility record

Release checklist

• ☐ The primary query is answered without an unsupported number.
• ☐ Every artifact and runtime is pinned.
• ☐ The representative task and failure policy are explicit.
• ☐ Lifecycle, cancellation, cleanup, and fallback are tested.
• ☐ User-content and network boundaries are documented.
• ☐ Result wording applies only to the recorded configuration.
• ☐ The page links to raw method or evidence when results are added.

Sources and related evidence

• Cove network audit
• Android log information disclosure guidance
• Android security and privacy overview
• Android Auto Backup

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Frequently asked questions