Privacy & compliance survey templates

Captures whether spa clients truly understand and freely consent to treatment risks, health disclosures, and photo or data use before their appointment — built for spa managers and compliance teams. The AI follow-up interview explores any hesitation or confusion behind risk-factor answers instead of settling for a checked box.

A pre-interview consent form that confirms participants understand what they're agreeing to — recording formats, how their words may be used, and their right to withdraw — plus an AI follow-up that catches confusion or hesitation before the real interview starts. Built for researchers running qualitative interviews, usability sessions, or customer discovery calls.

Measures how employees actually handle passwords, multi-factor authentication, updates, and suspicious messages day to day — not just what they know in theory. Built for IT and security teams auditing awareness programs, with an AI follow-up that reconstructs a real near-miss or incident instead of a hypothetical one.

Assesses a vendor's security controls, certifications, data handling, and incident history for procurement, security, and compliance teams running third-party risk reviews. An AI follow-up interview digs into the vendor's single most significant unresolved risk instead of accepting a checklist of certifications at face value.

Assesses how well employees and business travelers understand, trust, and can withdraw consent for travel-related data collection like itinerary sharing, location tracking, and duty-of-care programs. Built for privacy, compliance, and travel program teams, with an AI follow-up that surfaces the real reasons behind any hesitation or discomfort.

Captures a structured first-hand account of a suspected or confirmed security incident — what happened, what systems or data may be affected, and how contained it is — for IT, security, and compliance teams. An AI follow-up interview reconstructs the actual timeline of discovery and response instead of relying on a rushed written summary.

Captures explicit, informed consent before recording a call, interview, or research session, and checks participants' comfort with how the recording will be stored, shared, and retained. An AI follow-up interview surfaces any hesitations or conditions in the person's own words — built for research, support, and compliance teams who need a documented, genuine 'yes' before they hit record.

Captures how comfortable people are with having their photo, video, or quote used in marketing, training, or press materials — and under what conditions. The AI follow-up interview digs into the reasoning behind any hesitation so legal and marketing teams know exactly what conditions to honor, not just a yes/no checkbox.

A pre-appointment health-screening and informed-consent form for lash extension clients that captures allergy history, current medications, contact lens use, and aftercare understanding, plus a signed consent record. An AI follow-up interview clarifies any health disclosures or unclear aftercare answers before the technician proceeds, so studios document genuinely informed consent, not just a checked box.

Evaluates how clearly candidates or employees understood a background check disclosure and authorization request — including whether they felt informed, unrushed, and free to ask questions. An AI follow-up interview digs into the specific moment they hesitated or felt unclear, surfacing wording and process fixes that reduce compliance risk and candidate friction.

Measures how well employees recognize threats, follow security policy, and actually behave when something looks suspicious — not just what training they sat through. An AI follow-up interview digs into a real recent moment of hesitation or doubt to surface the gaps that policy audits miss.

Measures whether clients getting tattooed truly understood the risks, aftercare, and how their health data, ID, and photos will be stored before they signed — built for tattoo studios and body art compliance teams. The AI follow-up interview digs into the specific moment of signing to surface what was actually explained versus assumed.

Measures how employees feel about AI-based monitoring and quality-scoring tools at work — covering comfort, trust, fairness, and behavior change — with an AI follow-up that reconstructs one specific moment monitoring affected them instead of abstract opinions. Built for HR, legal, and workplace-technology teams evaluating or rolling out AI monitoring practices.

A complete consent flow for research studies: plain-language study information, granular consent statements with a comprehension check, signature capture, and contact enrollment. Built for IRB-style requirements — participants confirm they understand, not just that they scrolled.

Measures awareness, security perceptions, usage experience, and adoption intent for voice biometric authentication among banking customers. Suitable for financial institutions evaluating customer readiness for voice-based identity verification.

Measures which trust cues, social proof elements, and onboarding flows most effectively build user confidence during first-time website and app visits. Designed for UX researchers and product teams optimizing conversion funnels.

Measures how users interpret, trust, and engage with online platforms' transparency reports. Designed for researchers and compliance teams evaluating perceived clarity, credibility, and improvement opportunities.

Evaluates how clearly third-party risk disclosures and mitigations are communicated across teams. Designed for GRC, security, procurement, and other stakeholders who interact with vendor risk information, this instrument identifies gaps in accessibility, readability, and actionability to drive targeted improvements.

Measures which trust signals—testimonials, badges, and statistics—most influence online buyers' credibility perceptions and purchase intent. Designed for prospects actively researching products or services.

Evaluates consumer perceptions of SMS marketing consent flows and opt-out experiences to identify UX friction points and TCPA compliance gaps. Designed for brands seeking to improve trust, clarity, and regulatory readiness in their text messaging programs.

Surveys homeowners on their smart home comfort levels, device control preferences, and attitudes toward manufacturer default settings to inform product UX and setup experience design.

Assess the workload burden, pain points, and automation readiness of teams handling vendor security questionnaires. Designed for security, GRC, procurement, and IT professionals to inform process improvement priorities.

Measures consumer trust in organizational data handling, breach response expectations, and permission transparency. Use with general consumer panels to benchmark trust levels and identify priority improvements for privacy practices.

Evaluates team-level preparedness for SOC 2, ISO 27001, HIPAA, and other compliance audits. Use this to identify control gaps, evidence retrieval challenges, and resource priorities before your next audit cycle.

Measures user experience, satisfaction, and trust impact across the account and data deletion journey. Designed for product, privacy, and compliance teams seeking actionable feedback to reduce friction and support GDPR/CCPA requirements.

Measures how users interpret, evaluate, and act on app privacy nutrition labels using a concept-testing methodology with comprehension checks, trust and usefulness scales, and preference ranking. Suitable for UX researchers, privacy teams, and compliance professionals seeking to optimize label design.

Measures customer trust in data handling, perceived consent clarity, and privacy control preferences. Designed for e-commerce teams seeking to improve transparency, optimize opt-in flows, and inform GDPR/CCPA compliance strategies.

Measures employee perceptions of phishing simulation realism, behavioral responses, confidence changes, and training preferences to help security teams optimize their awareness programs.

Measures how well product administrators understand role-based access control concepts, permission scopes, and inheritance behaviors. Use this to identify confusion points, predictability gaps, and documentation needs that impact governance and security posture.

An internal stakeholder survey to assess organizational readiness for passkeys/passwordless authentication, surface security and UX risks, and align cross-functional teams on rollout priorities.

Measures user trust, comfort boundaries, and permission preferences for on-device personalization features. Designed for product and privacy teams validating data-handling approaches before launch.

Measures employee friction with multi-factor authentication prompts, backup method clarity, and step-up authentication impact to guide security UX improvements and reduce authentication-related workflow disruptions.

Measures how ecommerce customers perceive and respond to fraud-prevention steps during checkout, identifying the trade-offs between security friction and conversion to guide UX optimization.

Evaluates clarity, trust, friction points, and completion outcomes across electronic signature workflows. Designed for UX researchers and product teams seeking to identify and prioritize improvements in their signing experience.

Evaluates e-discovery software usability, workflow pain points, and feature priorities across the EDRM lifecycle. Designed for legal professionals, litigation support teams, and legal operations who use e-discovery platforms on active matters.

Measures consumer experiences with data subject access requests and expectations around response timelines, suitable for organizations assessing GDPR/CCPA compliance perceptions and identifying friction points in the DSAR process.

Measures research participants' comfort with anonymized data sharing, preferred licensing models, repository preferences, and required safeguards. Designed for IRB-compliant studies where researchers need informed consent data on secondary data use.

Measures how well users understand your data retention policy, evaluates the discoverability and usability of retention-related controls, and identifies preferred communication formats to inform UX and compliance improvements.

Measures user and organizational preferences for data storage location, cross-border transfer conditions, and governance trade-offs to inform product policy, compliance strategy, and trust-building initiatives.

An internal assessment for privacy, legal, and procurement professionals to evaluate the clarity, negotiability, and workflow efficiency of vendor Data Processing Agreements (DPAs), identifying bottlenecks and improvement priorities.

Evaluates the findability, clarity, effort, and trustworthiness of account deletion and data erasure flows. Designed for UX researchers and compliance teams auditing consumer-facing digital services against GDPR/CCPA standards.

Evaluates user experiences with data deletion, export, and portability flows across digital services. Designed for UX researchers and compliance teams seeking to identify friction points and improve data-control interfaces.

Measures consumer reactions to common dark-pattern design tactics and transparency preferences, providing actionable data on trust drivers for UX and product teams.

Measures customer trust, comfort, and consent preferences regarding international personal data transfers. Use this instrument to identify key drivers of acceptance, benchmark transparency satisfaction, and inform GDPR-aligned communication strategies.

Measures consumer perceptions of fairness, transparency, and trust in cookie paywalls versus privacy-friendly content-access alternatives. Designed for UX researchers, privacy professionals, and publishers seeking GDPR-aligned consent insights.

Measures user fatigue with GDPR cookie consent banners and preferences for privacy control interfaces. Designed for UX researchers and compliance teams seeking to improve consent experiences based on real browsing behavior.

Measures how web users perceive, interact with, and evaluate cookie consent banners and privacy preference centers. Ideal for UX researchers and compliance teams seeking to improve consent-interface clarity, fairness, and GDPR alignment.

Measures legal professionals' adoption levels, satisfaction, barriers, and safeguard requirements for AI-assisted contract review and redlining. Designed for legal operations, in-house teams, and law firm practitioners.

Assesses internal stakeholders' experience with contract workflow visibility, SLA adherence, and cycle times to identify process bottlenecks and prioritize improvements.

Evaluates how users discover, understand, and interact with cookie consent and preference-center interfaces. Use this survey to identify UX friction points, trust gaps, and feature priorities that affect compliance and conversion.