AI Training Practices
Training on Personal Data
Yes
Training on User Interactions
Yes
Training on Public Content
No
AI Data Sharing
Unclear
AI Use Cases
- Personalization — Improve search functionality and customize service experience based on user information.
- Recommendation — Make service or third-party service suggestions based on historical use and predictive models.
- Moderation — Process information in an automated fashion to prevent spam or abuse.
- Fraud Detection — Process information in an automated fashion to prevent abuse.
- Content Processing — Determine and rank the relevance of content, channels or expertise for an Authorized User.
Risk Assessment
Slack's privacy practices are generally robust, with a comprehensive policy and support for major regulations like GDPR and CCPA. However, as a workplace tool, administrators have significant control over user data, including messages and files. Extensive data collection and sharing with third-party service providers and integrated apps are also notable, requiring users to be mindful of their workspace's specific settings and policies.
Recommended Actions
- Understand your workspace administrator's data retention and access policies, as they often control your data on Slack.
- Review and manage third-party app integrations within your Slack workspace, as these can access your data.
- Utilize Slack's account settings and the dedicated data request form for managing your personal data and exercising your rights.
- Be aware of the type of information you share, as it may be accessible to your workspace administrators and potentially shared with integrated services.
AI Overview
Trains on user dataYes
Trains on interactionsYes
Opt-out availableNo
AI disclosureYes
Third-party AINo
AI Training Opt-Out
No opt-out mechanism available.