The Nonymize field guide
Practical guidance for safer AI workflows
Clear, practical guidance for preparing sensitive conversations and documents before AI—without losing the context that makes them useful.
Privacy · AI workflows · Client trustLatest resources
View all →- 01
General · Aug 19
What are Structured Outputs? And why they are just a gate on reliability and not privacy?
- 02
General · Aug 13
When it comes to AI tool usage, the “Not Used for Training” data promise does not mean you're out of the woods
- 03
General · Aug 11
The Biggest Companies in Tech Just Validated the Architecture We Built Nonymize On
General
Anonymization Is More Than Removing a Person's Name
Removing names from a client transcript isn't anonymization — indirect identifiers like employer, job title, city, and deal details can re-identify someone with no name in the document at all. Research from Carnegie Mellon found 87% of Americans are uniquely identifiable from just ZIP code, birth date, and gender, and a peer-reviewed 2019 study put re-identification at 99.98% with 15 attributes, even in incomplete datasets. Real anonymization means catching the full combination of identifiers, keeping replacements consistent across documents, and putting a human review step in front of anything that reaches an AI tool.
Read the guide →Put the guidance to work
Prepare your next document before it reaches AI.
Replace sensitive details, review every change, and leave with a clean copy.