How to Audit Export and Deletion Controls in an 18+ AI Companion
Focus query: 18+ AI companion deletion audit
This open worksheet turns a narrow adult-AI quality question into a reproducible review. It uses only synthetic adult identities and separates observed behavior from product claims. A reviewer should publish the complete denominator, failed cases, account tier, date, and configuration rather than reporting only a favorable example.
Research question
Can an adult user understand, export, correct, and remove intimate conversation or generation data across every relevant product surface?
Controlled setup
Use a synthetic account containing invented profile fields, a short chat, one generated image, one export request, and a unique marker string that can be searched after deletion.
Test protocol
- Map every data entry point from sign-up, chat, media generation, billing, support, and analytics disclosures.
- Distinguish hiding, clearing local history, account deactivation, and server-side deletion.
- Request an export and compare its fields with what was visible in the product.
- Complete the documented deletion path and record confirmations, delays, and exceptions.
- After the stated processing window, test whether the synthetic marker remains accessible through normal user controls.
Scoring rubric
| Metric | Operational definition | Record |
|---|---|---|
| Control discoverability | Steps required to find export and deletion controls | 0-4 plus written evidence |
| Scope clarity | Data categories explicitly covered | 0-4 plus written evidence |
| Completion evidence | Receipt or state change confirming the request | 0-4 plus written evidence |
| Residual visibility | Synthetic markers still visible after the stated window | 0-4 plus written evidence |
| Policy-product match | Observed controls consistent with published descriptions | 0-4 plus written evidence |
Use 0 for a material failure, 1 for mostly failing, 2 for mixed behavior, 3 for a repeatable pass with minor defects, and 4 for a repeatable pass with clear evidence. Never replace the raw count with the average score.
Downloadable evidence schema
Download the blank CSV test template. Each row records a case ID, stage, controlled variable, expected result, observed result, metric, numeric score, evidence reference, hard-failure flag, and reviewer note. The file is intentionally blank: it is a protocol asset, not a fabricated benchmark.
Hard-failure rule
A hard failure occurs when the user cannot complete the documented deletion route, a private mode is presented as deletion without support, or exported data reveals unexpected cross-account content.
Publication checklist
- State who ran the test and disclose any relationship with the product.
- Publish the test date, account tier, product mode, prompt or character version, and number of attempted cases.
- Keep failed examples and explain exclusions.
- Separate product facts, reviewer observations, and interpretations.
- Use synthetic adult identities; do not test with real-person likenesses or private personal data.
- Retest after model, memory, policy, pricing, or media-pipeline changes.
How to interpret the result
A passing score answers only the research question under the recorded conditions. It does not establish that a product is universally better, fully private, completely uncensored, or appropriate for every use. Compare methods and evidence before comparing averages. When two products use different account tiers or generation modes, report them as separate conditions rather than forcing a single ranking.
FAQ
Is this an independent Ponys.ai review?
No. This is a transparent, team-maintained test method. Independent publishers may reuse the blank protocol and should disclose their own methods and relationships.
Can the score be used as a marketing claim?
Only with the test date, denominator, configuration, and evidence. A number without those fields is not reproducible.
Why use synthetic adult cases?
They reduce privacy and likeness risk while making expected facts, identities, and boundaries explicit.
What should be cited?
Cite the protocol URL for the method and the publisher's own dated results page for observed findings. Do not cite the blank CSV as if it contained measured results.