Slack AI Training with Customer Data
Slack’s quiet use of customer messages, files, and other workspace data to train global AI/ML models—unless an organization explicitly opts out via email—is raising alarm over privacy, consent, and legal compliance. Commenters question how “data will not leak across workspaces” can be guaranteed, criticize default opt‑in for a paid enterprise product, and note potential conflicts with regulated industries and GDPR-style rules. Many call for self‑hosted or end‑to‑end‑encrypted alternatives, while others point out that some of Slack’s cited uses (search ranking, recommendations, emoji suggestions) could in theory be implemented with less invasive data practices.
Scope of Slack’s AI Training
- Slack’s policy says it uses “Customer Data” (messages, files, usage) to train “global models” for features like search ranking, channel recommendation, autocomplete, and emoji suggestions.
- Separate docs say customer data is not used to train large language models (LLMs) for “Slack AI”; those LLMs are hosted in-house and not updated with customer data.
- Some participants call this a “nothingburger” typical of long‑standing ML features; others argue the wording is vague and full of loopholes.
Opt‑Out vs Opt‑In and Friction
- Strong consensus that using private customer data for model training should be opt‑in, not opt‑out.
- The required opt‑out mechanism (admin must email support with a specific subject line) is seen as deliberately high‑friction and easy to miss, akin to burying notice “in a locked filing cabinet.”
- Multiple people share the exact email text they used and confirm Slack’s canned confirmation response.
Privacy, Security, and Legal Concerns
- Many see this as a serious risk for companies handling sensitive or regulated data (finance, healthcare, legal, IP‑heavy startups).
- Questions raised about:
- GDPR / “right to be forgotten” and whether models can practically “unlearn” specific users’ data.
- Whether “data will not leak across workspaces” is technically enforceable, especially even for non‑LLM classifiers and ranking models.
- The difference between “Slack can’t access content” vs “employees won’t,” with skepticism about the word “can’t.”
- Some expect large enterprise legal departments to push back or demand redlines; others think small and mid‑size customers will largely ignore it.
Trust, Ethics, and Business Model
- Strong sentiment that being a paying B2B customer should preclude being treated as free training data.
- Many argue this erodes trust and will drive some customers to alternatives or to self‑hosted, end‑to‑end‑encrypted tools.
- A minority defend participation as “helping build a better product,” while critics counter that users should be compensated or at least explicitly consent.
Alternatives and Responses
- Numerous suggestions to move to or consider Matrix/Element, Zulip, Mattermost, Rocket.Chat, Campfire, Nextcloud Talk, or even Signal; mixed views on Teams and Discord (both also distrusted).
- Some propose “poisoning” training data with junk; others call for regulation, boycotts, or legal challenges.