A support message can contain two different things: the task you want an AI model to perform, and details the model does not need to perform it.
“Draft a reply about a delayed delivery” is the task. A customer's email address in that same message may be unnecessary context.
I am building Sether around that boundary. It is an open-source library that replaces detected sensitive values with stable tokens before text goes to an LLM. Your application keeps the mapping and can restore recognized tokens in the response.
The useful question is not “did we add a privacy tool?” It is “what actually left the application?”
A practical evaluation has four parts:
Streaming adds another case: a value may be split across chunks. Include that in your tests rather than relying only on complete strings.
The token mapping also deserves attention. Decide which request or user owns it, who can read it, and how long it should exist. Redacting the outbound prompt is only one part of the application's data flow; logs, traces, attachments and downstream tools need their own review.
Sether is not a promise that every sensitive value will be detected, and installing it does not establish regulatory compliance. Detector selection, configuration and workflow evaluation matter.
The released library is MIT licensed. The hosted gateway is not part of this release.
Source and installation instructions: [https://github.com/raeven-co/sether](https://github.com/raeven-co/sether)
If you build AI support or internal-assistant workflows, which test would you want a redaction library to pass before you put it between your app and a model?
Disclosure: I am Sether's founder. This article was prepared with AI assistance using the project's documentation.