# Synthetic debugging-log preparation pack

Compare a raw fictional trace, a minimised version and the fields retained for diagnosis.

Entirely synthetic logs. The offline transformation is limited to this fixture and is not a production sanitizer.

Source: https://aona.ai/resources/guides/production-logs-ai-debugging/
Sources checked: 2026-09-21

| Field | Raw example | Prepared example |
| --- | --- | --- |
| Request correlation | SYNTHETIC_REQ_004 | [REQUEST_1] on both lines |
| Authorization | Fake bearer marker | [AUTH_REMOVED] |
| Customer email | person@example.invalid | [EMAIL_1] |
| Failure context | Missing quantity and two frames | Preserved |

## Review checklist

- [ ] Write the debugging question
  Retain only events and stack frames that contribute to it.
- [ ] Preserve useful relationships
  Use consistent replacements for repeated identifiers without keeping original values.
- [ ] Review the remaining excerpt
  A transformation is not an automatic disclosure approval or complete sensitive-data scan.

## Included example files

- README.md
- synthetic-raw.log
- expected-minimised.log
- prepare_example.py
- excerpt-review.md

## Source references

- OAIC: Use of commercially available AI products: https://www.oaic.gov.au/privacy/privacy-guidance-for-organisations-and-government-agencies/guidance-on-privacy-and-the-use-of-commercially-available-ai-products (2026-09-21)
- Claude Code: Data usage: https://code.claude.com/docs/en/data-usage (2026-09-21)
- Aona: AI security coverage: https://aona.ai/resources/ai-security-coverage/ (2026-09-21)

Use the worksheet within the relevant legal, contractual and technical scope. It is not a certification or a record of an installed-product test.