AI Privacy Data-Flow Mapper
Describe how data enters an AI workflow, crosses boundaries, gets logged and is eventually deleted.
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The fields below contain example values. Replace them with your own evidence; nothing is sent to a Ninth Post server.
How this tool works
This mapper creates a conceptual route from collection to the application, the model/provider boundary, possible logs or storage, and deletion. Sensitive data, third-party processing, retention, broad access and absent evidence create specific follow-up questions. Results are intended for a data-protection impact discussion and may be exported as a JSON review record. No prompts, files, IDs or actual personal data are required.
Limitations and responsible use
The browser-only form does not transmit or store the entries on Ninth Post servers. Avoid entering real personal information. Data protection requirements differ by country, purpose, processing role and effective dates; no automated output establishes regulatory compliance. Validate technical deletion and vendor contractual terms.
Continue the research
Read the related Ninth Post guide: AI Privacy Data-Flow Mapper — companion analysis. Review the primary methodology or framework source and our editorial standards. Results reflect assumptions, not independent testing.