Do they own your data? Sprig Privacy Policy Reviewed.
Sprig receives a 4/10 enterprise readiness score, marking it as partially ready for large organizations. Our comprehensive analysis reveals gaps in security, compliance, and scalability that enterprise buyers should consider before implementation.
Final Enterprise Readiness Rating: 4/10
⚠️ Partially ready (Reviewed 2026).
|
Area |
Verdict |
Notes |
|---|---|---|
|
Data Residency & Storage |
❌ High Risk |
Explicitly states data can be transferred and stored anywhere globally with weaker protections than home jurisdiction |
|
AI Model Use |
❌ High Risk |
No mention of AI model training, data usage for ML, or customer control over AI processing |
|
Data Minimization |
⚠️ Partial |
Collects standard business data but uses it for broad marketing and development purposes |
|
Privacy Controls |
⚠️ Partial |
Provides standard individual rights but no enterprise-level privacy controls or admin settings |
|
Compliance & Auditability |
✅ Good |
GDPR compliant with Data Privacy Framework certification, but no mention of SOC 2 or industry-specific compliance |
|
Consent Handling |
❌ High Risk |
No built-in consent workflows for end-user research participants, relies on customer responsibility |
|
Model Explainability |
❌ High Risk |
No mention of AI explainability, logging, or observability features |
|
Data Retention & Deletion |
⚠️ Partial |
Standard retention language with deletion rights, but no specific timeframes or auto-deletion |
|
Third-Party Sharing |
❌ High Risk |
Shares data with advertising partners for personalized advertising, completely inappropriate for business users |
⚠️ Recommendation for Enterprises:
Adopt Sprig with caution. Be especially careful if you handle:
- Confidential client communications
- Health, financial, legal, or regulated data
- Sensitive IP or trade secrets
- Competitive research insights
Instead, consider AI tools that:
- Offer enterprise plans without advertising partnerships
- Provide data residency controls
- Add SOC 2 Type II certification
- Include consent automation for research participants
- Clarify AI training data policies
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.
🔍 Sprig Privacy Policy – Enterprise Risk Assessment
Audience: Security-conscious enterprise organizations evaluating In-product user research and feedback platform for internal use in highly sensitive or regulated environments (e.g. legal, healthcare, finance, tech/IP-heavy orgs).
⚠️ Where Sprig Falls Short – Critical Gaps
🔒 1. Data Residency & Storage
Quote: "All information processed by us may be transferred, processed, and stored anywhere in the world, including, but not limited to, the United States or other countries, which may have data protection laws that are different from the laws where you live."
Risk: For enterprises in regulated industries, this creates compliance nightmares. EU financial data could end up in countries with no data protection laws, violating GDPR and industry regulations.
Enterprise Issue:
- No data residency controls
- Global transfers without adequate safeguards
- Cannot guarantee compliance with local data laws
Verdict: ❌ Fails basic data sovereignty requirements
🧠 2. AI Model Use
Risk: The policy is silent on whether customer feedback data trains their AI models. For enterprises, this could mean confidential user research becomes training data for competitors to benefit from.
Enterprise Issue:
- No transparency on AI training data usage
- No opt-out mechanisms for AI training
- Cannot bring own models or control AI processing
Verdict: ❌ Complete blindness on AI training practices
📊 3. Data Minimization
Quote: "We may use personal information and other information about you to create de-identified and/or aggregated information, such as de-identified demographic information, de-identified location information, information about the device from which you access our Services, or other analyses we create."
Risk: While individual data points seem reasonable, the aggregation and analysis for unspecified business purposes creates risk that enterprise usage patterns become competitive intelligence.
Enterprise Issue:
- Broad secondary use permissions
- Aggregated data analysis without clear limits
- Marketing use of business relationship data
Verdict: ⚠️ Reasonable data collection scope but overly broad purposes
⚙️ 4. Privacy Controls
Quote: "If you have any questions about our marketing practices, you may contact us at any time as set forth in 'Contact Us' below."
Risk: No workspace-level controls means enterprises cannot centrally manage privacy settings for their teams, creating compliance gaps when employees individually control enterprise research data.
Enterprise Issue:
- No enterprise admin controls
- Individual-level privacy management only
- No bulk privacy configuration options
Verdict: ⚠️ Basic consumer controls insufficient for enterprise needs
📦 5. Compliance & Auditability
Quote: "Sprig complies with the EU-U.S. Data Privacy Framework and Swiss-U.S. Data Privacy Framework as set forth by the U.S. Department of Commerce regarding the collection, use, and retention of personal information transferred from the European Union and Switzerland to the United States."
Risk: While GDPR compliance is good, lack of SOC 2 Type II means no third-party validation of security controls. Healthcare and financial enterprises need HIPAA/SOX compliance verification.
Enterprise Issue:
- No SOC 2 Type II certification mentioned
- Missing industry-specific compliance (HIPAA, SOX)
- No audit trail guarantees for data access
Verdict: ✅ Strong compliance framework but missing key certifications
📬 6. Consent Handling
Quote: "Please Note: This Privacy Notice does not apply to any of the personal information that our customers may collect and process using our Services ('Customer Data'). Our customers' respective privacy policies govern their collection and use of Customer Data."
Risk: For a user research platform, having no consent automation is a massive liability. Enterprises conducting research could violate GDPR/CCPA if Sprig doesn't handle participant consent properly.
Enterprise Issue:
- No automated consent collection for research participants
- Customer bears full consent compliance burden
- No consent recording or management tools
Verdict: ❌ Dangerous consent gaps for user research platform
🔍 7. Model Explainability
Risk: For AI-powered research insights, enterprises need to understand how conclusions are reached. Without explainability, research findings become black boxes unsuitable for business decisions.
Enterprise Issue:
- No AI decision logging
- Cannot audit AI recommendations
- No transparency into model behavior
Verdict: ❌ Zero transparency on AI decision-making
🧼 8. Data Retention & Deletion
Quote: "We store the personal information we collect as described in this Privacy Notice for as long as you use our Services or as necessary to fulfill the purpose(s) for which it was collected, provide our Services, resolve disputes, establish legal defenses, conduct audits, pursue legitimate business purposes, enforce our agreements, and comply with applicable laws."
Risk: The broad 'legitimate business purposes' language could justify indefinite retention. Enterprises need predictable data lifecycle management for compliance.
Enterprise Issue:
- No automatic data expiration
- Overly broad retention justifications
- No configurable retention policies
Verdict: ⚠️ Vague retention with reasonable deletion rights
🤝 9. Third-Party Sharing
Quote: "We may share your personal information with third-party advertising partners. These third-party advertising partners may set Technologies and other tracking tools on our Services to collect information regarding your activities and your device (e.g., your IP address, cookie identifiers, page(s) visited, location, time of day)."
Risk: Enterprise user research data should never feed advertising networks. This creates massive competitive intelligence leaks and violates the confidentiality enterprises expect from research tools.
Enterprise Issue:
- Advertising partner data sharing
- Third-party tracking on enterprise accounts
- No enterprise exemption from advertising ecosystem
Verdict: ❌ Unacceptable advertising partnerships for enterprise data
✅ What Sprig Does Right (Credit Where It's Due)
- GDPR compliance with individual rights
- Data Privacy Framework certification
- Clear distinction between customer data and platform data
- Reasonable security incident notification procedures
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.