Do they own your data? Productboard Privacy Policy Reviewed.

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Final Enterprise Readiness Rating: 6/10

⚠️ Partially ready (Reviewed 2026).

⚠️ Recommendation for Enterprises:

Adopt Productboard with caution. Be especially careful if you handle:

• Confidential client communications
• Health, financial, legal, or regulated data
• Sensitive IP or trade secrets

Instead, consider AI tools that:

• Offer full control over data use and location
• Provide AI transparency and bring-your-own model options
• Support SOC 2 Type II, HIPAA compliance
• Have enterprise-grade consent automation and privacy 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.

🔍  Productboard Privacy Policy – Enterprise Risk Assessment

Audience: Security-conscious enterprise organizations evaluating Product management platform for roadmaps and customer feedback for internal use in highly sensitive or regulated environments (e.g. legal, healthcare, finance, tech/IP-heavy orgs).

⚠️ Where Productboard Falls Short – Critical Gaps

🔒  1. Data Residency & Storage

Quote: "We primarily store personal information about Website Visitors and Subscribers within the European Economic Area (the 'EEA') and in the United States. To facilitate our global operations, we may transfer and access such personal information from around the world"

Risk: Enterprises in regulated industries need guaranteed data residency control. Global transfers without customer consent create compliance risks for healthcare, finance, and government sectors.

Enterprise Issue:

• No customer control over data location
• Global transfers for operational convenience
• No on-premise or VPC deployment options mentioned

Verdict: ⚠️ Limited geographic control

🧠  2. AI Model Use

Quote: "Search & analyze customer feedback 6x faster. Productboard Spark - the AI for PMs."

Risk: The complete absence of AI data handling in the privacy policy while advertising AI features is a massive red flag. Enterprises need to know if their sensitive product roadmaps are being used to train external models.

Enterprise Issue:

• No disclosure of AI data processing
• Unknown if customer data trains models
• No bring-your-own-model options
• Zero transparency on AI providers

Verdict: ❌ Zero AI transparency

📊  3. Data Minimization

Quote: "We and our authorized partners use cookies and other information gathering technologies for a variety of purposes... We partner with third parties to manage our advertising for our Website and Services"

Risk: While core product data collection is reasonable, extensive tracking partnerships and advertising data sharing create unnecessary exposure for enterprise customers handling confidential information.

Enterprise Issue:

• Third-party advertising partnerships
• Broad analytics collection
• Cookie tracking for marketing purposes

Verdict: ⚠️ Standard collection practices

⚙️  4. Privacy Controls

Quote: "If you do not want to receive marketing email communications from us, you can opt-out by clicking on the 'unsubscribe' link... Subscribers to our Services may update or change their Account Information"

Risk: Privacy controls are designed for individual consumers, not enterprise administrators who need to manage privacy settings across entire organizations and ensure compliance at scale.

Enterprise Issue:

• No mention of workspace-level privacy controls
• Limited enterprise admin capabilities
• Individual opt-out model not scalable

Verdict: ⚠️ Individual-focused, not enterprise-grade

📦  5. Compliance & Auditability

Quote: "Productboard has certified to the EU-U.S. Data Privacy Framework, the UK Extension to the EU-U.S. DPF, and the Swiss – U.S. Data Privacy Framework... we generally rely on our DPF certification or Standard Contractual Clauses"

Risk: While regulatory compliance is solid, the absence of SOC 2 Type II or ISO 27001 mentions and lack of audit trail details limit enterprise confidence in security controls.

Enterprise Issue:

• No SOC 2 Type II certification mentioned
• No ISO 27001 certification
• Limited audit trail information

Verdict: ✅ Strong regulatory foundation

📬  6. Consent Handling

Quote: "We collect certain information automatically from Users through cookies and other tracking technologies when they use a Subscriber's Account, subject to the applicable law's consent requirements"

Risk: While legally compliant, the consent framework doesn't address enterprise needs for managing consent across teams or ensuring compliance when handling third-party data.

Enterprise Issue:

• No enterprise consent management tools
• Limited consent granularity
• No automated compliance workflows

Verdict: ⚠️ Basic consent mechanisms

🔍  7. Model Explainability

Risk: Enterprises need to understand how AI systems process their data, especially for regulated industries where algorithmic decisions must be explainable and auditable.

Enterprise Issue:

• No AI model transparency
• No logging or observability mentioned
• No explanation of AI decision-making

Verdict: ❌ Complete AI blackout

🧼  8. Data Retention & Deletion

Quote: "We will retain personal information we collect from you where we have a justifiable business need to do so... After that time, we will either delete or anonymize it, or, if this is not possible... then we will securely store your personal information and isolate it from any further processing until deletion is possible"

Risk: While retention policies are well-defined, enterprises need more control over retention periods and guaranteed deletion timelines, especially for post-contract scenarios.

Enterprise Issue:

• No customer control over retention periods
• Vague 'business need' justification
• No guaranteed deletion timelines

Verdict: ✅ Comprehensive retention framework

🤝  9. Third-Party Sharing

Quote: "We disclose identifiers with data analytics service providers, social networks, payment processors, customer support partners, events and promotions partners, and fraud prevention partners"

Risk: While transparency is appreciated, the extensive list of third-party recipients creates multiple points of exposure for sensitive enterprise data, especially concerning social networks and advertising partners.

Enterprise Issue:

• Data shared with social networks
• Extensive third-party partnerships
• No customer control over sharing preferences

Verdict: ⚠️ Transparent but extensive sharing

✅ What Productboard Does Right (Credit Where It's Due)

• Strong GDPR compliance and Data Privacy Framework certification
• Comprehensive privacy policy with clear data processing explanations
• Good data retention and deletion framework
• Transparent disclosure of third-party data sharing
• Proper legal basis documentation for data processing

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.