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Short answer: Slack says it may analyze customer data—including messages, content and files—to develop and train predictive machine-learning models for recommendations, search ranking and personalization. Slack separately says it does not use customer data to train large language models (LLMs) or other generative-AI models. An authorized workspace or organization owner can request that the workspace’s data be excluded from Slack’s global ML-model training.
This distinction matters. “Slack uses data from your chats” is a fair description of the predictive-ML policy, but it does not mean Slack is feeding every conversation into a general-purpose chatbot such as ChatGPT.
What Slack disclosed
As of August 18, 2026, Slack’s Privacy Principles say Slack systems may analyze Customer Data, usage information and other information described in its privacy policy and customer agreements to improve and update the service. The examples of Customer Data expressly include messages, content and files.
Slack gives examples such as channel recommendations, emoji recommendations, search and relevance ranking, and other personalized product experiences. These are predictive or recommendation systems, not necessarily conversational LLMs.
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Slack’s public pages do not provide a complete, feature-by-feature list showing which message fields, files or derived representations are used in each model. The defensible reading is that Slack acknowledges broad analysis of Customer Data for certain model-development purposes—not that every message is copied verbatim into every training set.
Predictive machine learning is not the same as generative-AI training
| Term | What it means here | Slack’s stated position |
|---|---|---|
| Predictive ML | Models that rank, recommend, classify or personalize results. | Customer Data may be used to develop and train global models, subject to a customer opt-out. |
| Generative AI / LLM training | Updating a language model’s parameters with examples so it can generate text. | Slack says Customer Data is not used to train LLMs or generative-AI models. |
| Inference | Sending information to an already-trained model to produce an answer or ranking. | Slack AI can retrieve permitted workspace content at request time. |
| Retrieval-augmented generation (RAG) | Relevant content is placed in an AI request’s context without retraining the underlying LLM. | Slack describes this architecture for Slack AI. |
| Global model | A model trained across customers and used to improve Slack’s general service. | Owners can request exclusion of their Customer Data. |
| Workspace-specific improvement | Improvements or personalization associated with one customer’s workspace. | Slack says some use may continue after a global-model opt-out. |
Slack’s AI Principles say Slack has used machine learning for features that predate its generative-AI products. Its engineering material describes recommendation and ranking systems that can rely heavily on usage metadata and numerical signals. That does not cancel the privacy page’s broader statement that messages, content and files may be analyzed; it means different systems can use different inputs.
Is Slack training a chatbot on private conversations?
Slack says no for LLM and generative-AI training. Slack says Customer Data is not used to train LLMs. For Slack AI, it says relevant information is retrieved for an individual request and sent to an LLM hosted within Slack-controlled cloud infrastructure. Slack’s security documentation says the model provider does not retain or train on that data after processing.
That is an inference-time safeguard, not a promise that Slack AI cannot read content the requesting user is allowed to access. Slack says AI features respect existing channel and message permissions, so a user should not receive content from a private channel or direct message that the user could not ordinarily open.
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Do not confuse that access control with the separate question of model development. “Slack AI cannot show me a DM I cannot access” does not establish that private messages are categorically excluded from every predictive-ML process.
Are private channels and direct messages included?
Slack’s predictive-ML language is broad. It refers to Customer Data and lists messages, content and files without limiting the statement to public channels. The available public documentation therefore does not support saying that private channels or direct messages are automatically excluded.
At the same time, Slack has not published a complete inventory of exactly which private-message or file content is used by each model. For a regulated or highly confidential workspace, ask Slack for a written explanation tied to your agreement rather than assuming either total inclusion or total exclusion.
How to opt out of Slack’s global ML models
Slack describes this as an owner-initiated request, not an ordinary member setting. Follow these steps:
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- Email [email protected].
- Use the subject line Slack Global model opt-out request.
- Include the workspace or organization URL.
- Keep Slack’s confirmation that the opt-out was completed.
Slack’s instructions appear on its Privacy Principles page. An ordinary employee generally cannot unilaterally opt out a company workspace.
What the opt-out does—and does not—change
Slack describes the request as excluding Customer Data from helping train Slack global models. Slack also says the customer may continue receiving benefits from globally trained models and that data may still be used to improve the experience on that customer’s own workspace.
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- It does address: the specific global-model training use described by Slack.
- It does not necessarily address: all analytics, personalization, security processing, backups, retention, exports or workspace-specific improvement.
- It does not publicly promise: retroactive deletion of information already incorporated into a trained model.
- It does not automatically cover: data sent to third-party Slack apps under their own permissions and privacy policies.
When requesting the opt-out, ask Slack to confirm whether it applies to one workspace or an entire organization, whether it is prospective only, and what workspace-specific processing remains.
Opt-out, Slack AI controls and permissions are separate
Disabling Slack AI features can prevent members from using functions such as AI search or summaries, depending on the controls available to your plan. It is not the same as requesting exclusion from predictive global-model training. Likewise, permission checks determine what a user may retrieve during an AI request; they do not answer whether data may be analyzed for model development.
What Slack’s contract language adds
Slack’s Supplemental Terms (last updated February 27, 2026) use broader language about using data “to train models for use by the services and features that Customer has access to.” The terms direct customers to the Privacy Principles for the operational explanation and opt-out process.
The two documents should be read together. The broader contractual wording is not proof that Slack trains a general-purpose LLM on customer chats, while the privacy page is not a blanket promise that no model-development processing occurs. The agreement governing your workspace, including any enterprise addenda, is the controlling document for your organization.
What Slack says about employee and provider access
Slack says it has technical controls intended to prevent employees from accessing underlying customer content while developing or analyzing AI/ML models. It also says customer data remains in Slack-controlled infrastructure and that model providers do not retain or train on data supplied for an AI request.
Those are Slack’s stated controls, not independent verification that no employee or third party could ever access content in any circumstance. Review audit reports, contractual commitments and your incident-response requirements if that distinction matters to your risk assessment.
Third-party apps are a separate privacy review
Slack’s own no-LLM-training statement does not automatically govern every installed integration. Review each app’s requested scopes, data flows, retention terms and privacy policy. Slack’s Developer Policy prohibits apps from using Slack data to train an LLM, but administrators should still evaluate apps individually and remove unnecessary access.
Should you switch platforms?
Switching platforms can change who operates the infrastructure, but it does not automatically produce “no AI training.” Compare the actual controls and operating responsibilities.
| Option | What it can improve | Trade-off |
|---|---|---|
| Stay with Slack and request the opt-out | Addresses the documented global-model concern while preserving integrations and workflows. | Requires an owner request and does not stop every form of processing. |
| Stay with Slack and restrict native AI | Reduces use of Slack AI features by members. | Separate from the predictive-ML opt-out. |
| Microsoft Teams or Google Chat | May simplify identity, compliance and bundle economics when the organization already uses Microsoft 365 or Google Workspace. | Still hosted services with their own AI, telemetry and contractual terms. |
| Mattermost or Rocket.Chat | Self-hosted, private-cloud or specialized enterprise options can increase infrastructure and jurisdictional control. | Your organization takes on hosting, patching, backups, monitoring and AI configuration. See Mattermost’s plans and Rocket.Chat’s plan documentation. |
| Element / Matrix | Federation, open protocols, encryption and deployment flexibility. | Administration and ecosystem fit may be less convenient than Slack. See Element’s plans. |
A self-hosted service can give you more control over infrastructure and jurisdiction, but it also makes your team responsible for encryption, identity, retention, backups, vulnerability management and any AI service attached to the deployment.
Administrator checklist
- Identify the Org Owner, Workspace Owner or Primary Owner who can submit the request.
- Email the exact opt-out subject and workspace URL to Slack.
- Save written confirmation and clarify scope: workspace, organization and effective date.
- Ask whether the request is prospective and whether previously trained models are affected.
- Review the customer agreement, retention settings, exports and data-processing terms.
- Audit every Slack app and integration separately.
- Restrict or disable native AI features where appropriate.
- Recheck Slack’s policies after material product or contract changes.
Bottom line
Slack’s current public position is nuanced: it may use Customer Data—including messages, content and files—to develop predictive ML models, while saying it does not use that data to train generative-AI models or LLMs. The practical control is an owner-requested opt-out from Slack’s global ML models, not a universal “never process our data” switch. Companies with stricter isolation requirements should combine that request with app audits, contract review and an honest comparison of hosted versus self-managed alternatives.
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