Do they own your data? Loom Privacy Policy Reviewed.
Loom receives a 6/10 enterprise readiness score - partially ready for business use. While excelling at video messaging and collaboration, gaps in advanced security features and administrative controls limit its enterprise appeal. Discover if Loom fits your organization's needs.
Final Enterprise Readiness Rating: 6/10
⚠️ Partially ready (Reviewed 2026).
|
Area |
Verdict |
Notes |
|---|---|---|
|
Data Residency & Storage |
⚠️ Partial |
References Trust Center but no specific data residency commitments in policy. Mentions global operations without location controls. |
|
AI Model Use |
❌ High Risk |
Uses AI for support responses and security detection but provides no details on models, training data, or enterprise controls. |
|
Data Minimization |
❌ High Risk |
Collects extensive behavioral data including collaboration patterns, @mention analysis, and video analytics without clear minimization controls. |
|
Privacy Controls |
✅ Good |
Provides detailed individual privacy controls and opt-out mechanisms, though enterprise admin controls are less clear. |
|
Compliance & Auditability |
✅ Excellent |
Demonstrates comprehensive compliance with GDPR, Data Privacy Framework, and provides detailed legal bases for processing. |
|
Consent Handling |
⚠️ Partial |
Provides consent mechanisms but no mention of automated recording consent workflows or enterprise consent management. |
|
Model Explainability |
❌ High Risk |
Uses AI for multiple purposes but provides no transparency into model decisions, logging, or explainability features. |
|
Data Retention & Deletion |
✅ Good |
Provides clear retention timelines and deletion processes, though some data persists for team functionality. |
|
Third-Party Sharing |
⚠️ Partial |
Shares data with partners, service providers, and third-party apps but provides clear disclosure and some user controls. |
⚠️ Recommendation for Enterprises:
Adopt Loom with caution. Be especially careful if you handle:
- Highly confidential client communications
- HIPAA-covered health data
- Financial trading information
- Attorney-client privileged content
Instead, consider AI tools that:
- Provide clear data residency guarantees
- Offer AI model transparency and controls
- Support enterprise-grade consent automation
- Enable strict third-party sharing controls
Better Alternative:
✅ BuildBetter.ai — GDPR, SOC 2 Type 2, and HIPAA compliant
✅ Zero training on customer data
✅ You own your data. Fully opt-in privacy model.
🔍 Loom Privacy Policy – Enterprise Risk Assessment
Audience: Security-conscious enterprise organizations evaluating Video messaging and screen recording platform for async communication for internal use in highly sensitive or regulated environments (e.g. legal, healthcare, finance, tech/IP-heavy orgs).
⚠️ Where Loom Falls Short – Critical Gaps
🔒 1. Data Residency & Storage
Quote: "We collect information globally and may transfer, process, and store your information outside of your country of residence, to wherever we or our third-party service providers operate for the purpose of providing you the Services."
Risk: Enterprises in regulated industries need guaranteed data residency controls. Global data transfers without explicit location controls violate many compliance requirements.
Enterprise Issue:
- No guaranteed data residency options
- Potential cross-border data transfers
- Lacks region-specific deployment options
Verdict: ⚠️ Vague on data location guarantees
🧠 2. AI Model Use
Quote: "We may also use generative artificial intelligence in responding to your support related requests. Detection and response may leverage generative artificial intelligence or machine learning tools."
Risk: Enterprises need to know which AI models process their data, where those models are hosted, and whether their data trains external models. Complete opacity is unacceptable for sensitive data.
Enterprise Issue:
- No disclosure of which AI models are used
- No opt-out from AI processing
- No bring-your-own-model options
Verdict: ❌ AI usage without transparency or controls
📊 3. Data Minimization
Quote: "We also collect information about the teams and people you work with and how you work with them, like who you collaborate with and communicate with most frequently. We automatically analyze recent interactions among users and how often they @mention one another."
Risk: This level of behavioral surveillance creates compliance risks and potential privacy violations. Enterprises need granular controls over what behavioral data is collected and processed.
Enterprise Issue:
- Extensive behavioral profiling
- No granular collection controls
- Surveillance-level data gathering on work patterns
Verdict: ❌ Excessive data collection scope
⚙️ 4. Privacy Controls
Quote: "You can exercise some of the choices by logging into the Services and using settings available within the Services or your account. You can control whether you receive these communications as described below."
Risk: Individual controls are good but enterprises need admin-level policy enforcement across all users. Relying on individual user choices creates compliance gaps.
Enterprise Issue:
- Limited enterprise admin controls mentioned
- Relies on individual user choices
- No clear workspace-level policy enforcement
Verdict: ✅ Comprehensive individual controls but limited enterprise admin features
📦 5. Compliance & Auditability
Quote: "Atlassian complies with the EU-U.S. Data Privacy Framework (EU-U.S. DPF), the UK Extension to the EU-U.S. DPF and the Swiss-U.S. Data Privacy Framework (Swiss-U.S. DPF) as set forth by the U.S. Department of Commerce."
Risk: Strong compliance framework provides good foundation for enterprise use, though specific SOC 2 and HIPAA certifications aren't detailed in this policy.
Enterprise Issue:
- SOC 2 Type II status unclear
- HIPAA compliance not explicitly mentioned
- Industry-specific certifications unclear
Verdict: ✅ Strong compliance framework
📬 6. Consent Handling
Quote: "With your consent, we may post your name alongside the testimonial. We often display personal testimonials of satisfied customers on our public websites."
Risk: Video recording platforms need automated consent workflows to ensure legal compliance. Manual consent processes create liability for enterprises.
Enterprise Issue:
- No automated recording consent workflows
- Limited enterprise consent management
- Potential recording compliance gaps
Verdict: ⚠️ Basic consent mechanisms, lacks recording workflow automation
🔍 7. Model Explainability
Risk: Enterprises need to understand how AI systems process their data and make decisions. Complete lack of AI transparency creates audit and compliance risks.
Enterprise Issue:
- No AI decision logging
- No model explainability features
- Black box AI processing
Verdict: ❌ Black box AI operations
🧼 8. Data Retention & Deletion
Quote: "If your account is deactivated or disabled, some of your information and the content you have provided will remain in order to allow your team members or other users to make full use of the Services."
Risk: Retention policies are reasonable but persistent data after account deletion could create compliance issues for enterprises with strict data lifecycle requirements.
Enterprise Issue:
- Some data persists after account deletion
- Limited configurable retention periods
- Team functionality overrides deletion in some cases
Verdict: ✅ Reasonable retention policies with deletion options
🤝 9. Third-Party Sharing
Quote: "We work with a global network of partners who provide consulting, implementation, training and other services around our products. We may disclose your information to these third parties in connection with their services."
Risk: Extensive third-party data sharing creates attack surface and compliance complexity. Enterprises need strict controls over which third parties can access their data.
Enterprise Issue:
- Broad partner data sharing
- Third-party app data access
- Limited enterprise controls over sharing
Verdict: ⚠️ Extensive third-party ecosystem with limited controls
✅ What Loom Does Right (Credit Where It's Due)
- Comprehensive GDPR compliance with clear legal bases
- Detailed Data Privacy Framework certification
- Transparent disclosure of data collection practices
- Robust individual privacy controls and opt-out mechanisms
- Clear retention and deletion policies
Disclaimer: This evaluation is based solely on publicly available information and documentation. For formal enterprise vetting, always request a vendor's latest DPA, security whitepaper, and third-party audit reports.