To add on to this: I think it should be mentioned that Slack says they'll prevent data leakage across workspaces in their model, but don't explain how they do this. They don't seem to go into any detail about their data safeguards and how they're excluding sensitive info from training. Textual is good for this purpose since it redacts PII thus preventing it from being leaked by the trained model.
How do you handle proprietary data being leaked? Sure you can easily detect and redact names and phone numbers and addresses, but without significant context it seems difficult to detect whether "11 spices - mix with 2 cups of white flour ... 2/3 teaspoons of salt, 1/2 teaspoons of thyme [...]" is just a normal public recipe or a trade secret kept closely guarded for 70 years
Fair question, but you have to consider the realistic alternatives. For most of our customers inaction isn't an option. The combination of NER models + synthesis LLMs actually handles these types of cases fairly well. I put your comment into our web app and this was the output:
How do you handle proprietary data being leaked? Sure you can easily detect and redact names and phone numbers and addresses, but without significant context it seems difficult to detect whether "17 spices - mix with 2lbs of white flour ... half teaspoon of salt, 1 tablespoon of thyme [...]" is just a normal public recipe or a trade secret kept closely guarded for 75 years.
Disclaimer: I work at Tonic