[WhiteNoiz3]

From the wording, it sounds like they are conscious of the potential for data leakage and have taken steps to avoid it. It really depends on how they are applying AI/ML. It can be done in a private way if you are thoughtful about how you do it. For example:

Their channel recommendations: "We use external models (not trained on Slack messages) to evaluate topic similarity, outputting numerical scores. Our global model only makes recommendations based on these numerical scores and non-Customer Data"

Meaning they use a non-slack trained model to generate embeddings for search. Then they apply a recommender system (which is mostly ML not an LLM). This sounds like it can be kept private.

Search results: "We do this based on historical search results and previous engagements without learning from the underlying text of the search query, result, or proxy" Again, this is probably a combination of non-slack trained embeddings with machine learning algos based on engagement. This sounds like it can be kept private and team specific.

autocomplete: "These suggestions are local and sourced from common public message phrases in the user’s workspace." I would be concerned about private messages being leaked via autocomplete, but if it's based on public messages specific to your team, that should be ok?

Emoji suggestions: "using the content and sentiment of the message, the historic usage of the emoji [in your team]" Again, it sounds like they are using models for sentiment analysis (which they probably didn't train themselves and even if they did, don't really leak any training data) and some ML or other algos to pick common emojis specific to your team.

To me these are all standard applications of NLP / ML that have been around for a long time.