Flint Inc (flintk12.com)

|

Last Updated: April 14, 2026

Flint Data Protection Impact Assessment (DPIA)

Flint Data Protection Impact Assessment (DPIA)

Description of processing

Description of processing

AI system data processing

AI system data processing

Flint K12's platform handles educational information through its technology infrastructure with AI capabilities. The service relies on Anthropic, OpenAI, Replicate (image and video generation), E2B (code execution), and Exa (web search) as AI sub-processors, with data storage managed through Supabase (PostgreSQL database on AWS), application hosting through Vercel, and workflow automation through Fly.io.

The system develops interactive learning experiences using teacher materials and student engagement data while maintaining privacy safeguards.

Data handling happens via secure connections to Student Information Systems and Learning Management Systems through Edlink. AI tools work with this information exclusively for customizing learning experiences based on teacher and student input. No student data is used for AI model training or stored beyond its educational purpose.*

Purposes of processing

Purposes of processing

The platform handles personal information for several core functions:

  • User login management through Google, Microsoft, and Circle SSO authentication

  • Monitoring learning advancement

  • Enabling student-AI tool communication

  • Generating tailored educational material (text, images, videos, code visualizations)

  • Maintaining academic records

  • Supporting school administrative operations

Categories of personal data

Categories of personal data

The system collects multiple data types:

Identification Information

  • First and last names

  • Email addresses

  • Google/Microsoft profile pictures (when available)

  • Authentication credentials

Educational Information

  • Class enrollment records

  • Learning advancement metrics

  • Assessment outcomes

System Usage Data

  • Browser specifications and operating system details

  • Viewed pages and clicked links

  • Feature engagement duration

  • Language settings

  • Device identification

  • Internet protocol address

Geolocation Data (derived from IP address)

  • City-level location

  • Country

  • Region/state

Analytics Event Data

  • User interaction events

  • Feature usage patterns

  • Session data

Student-Generated Content

  • AI chatbot conversation logs

  • Work submissions

  • Written essays

  • Audio files (where applicable)

  • Code submissions

  • Generated images and videos

Data flows and storage

Data flows and storage

All operations occur within United States territory. Primary data storage uses Supabase (PostgreSQL on AWS) servers within the United States with redundancy across different U.S. regions.

Data movement begins with school system integration (via Edlink), moves through approved processors, and employs encrypted connections with controlled access privileges.

Retention periods

Retention periods

Active accounts maintain data indefinitely unless the user requests deletion. Upon deletion requests, all associated data is removed within 30 days. Backup systems preserve data according to redundancy requirements. Schools may request institution-wide data removal anytime.

Active accounts maintain data indefinitely unless the user requests deletion. Upon deletion requests, all associated data is removed within 30 days. Backup systems preserve data according to redundancy requirements. Schools may request institution-wide data removal anytime.

Sub-Processors

Sub-Processors

The following third parties process personal data on behalf of Flint:

AI Processing Services

AI Processing Services

Provider

Purpose

Data Processed

Anthropic

AI chat, content generation

User prompts, conversation context

OpenAI

AI chat, content generation

User prompts, conversation context

Replicate

Image/video generation

User prompts, uploaded images

E2B

Code execution sandbox

User-submitted code

Exa

Web search for AI

Search queries

Infrastructure & Hosting

Infrastructure & Hosting

Provider

Purpose

Data Processed

Supabase

Database hosting (AWS)

All application data

Vercel

Application hosting

Request logs, application data

Fly.io

Workflow automation

Operational data

Third-Party Integrations

Third-Party Integrations

Provider

Purpose

Data Processed

Edlink

SIS/LMS integration

Student rosters, class data

Sentry

Error tracking

Error logs, user context

SendGrid

Email delivery

Email addresses, message content

Intercom

Customer support

User profiles, support conversations

PostHog

Product analytics

Usage events, user properties

Mixpanel

Product analytics

Usage events, user properties

Slack

Internal notifications

Aggregated alerts

Google Workspace

Calendar/email integration

Meeting data, email content

Google Analytics

Web analytics

Page views, user sessions, anonymized events

Google Ads

Advertising

Anonymized conversion data

ConvertAPI

Document conversion

Uploaded documents

Datalab

Document processing

Uploaded documents

HubSpot

CRM

Contact names, emails, school associations

GitHub

Code repository

Issue/PR content (may reference user data)

Necessity assessment

Necessity assessment

Justification for data collection

Justification for data collection

Authentication details create the foundation for account protection and access management. Educational information enables core service delivery and progress assessment. Usage data maintains system functionality and helps identify technical improvements. Geolocation data (city/country level, derived from IP) enables district-level analytics reporting.

Processing for proportionality

Processing for proportionality

Data gathering stays strictly limited to educational purposes with no marketing or commercial use of student data. Collection scope aligns with school requirements and learning objectives.

Evaluation of less intrusive alternatives

Evaluation of less intrusive alternatives

The current method represents minimum data processing necessary to achieve the platform's educational objectives. Reducing data collection would undermine educational functionality. The approach follows data minimization principles while preserving service quality.

Risk assessment

Risk assessment

Identification of potential risks to data subjects

Identification of potential risks to data subjects

For Student Users

  • Unauthorized access to records

  • Misuse of student-created materials

  • Privacy concerns regarding AI interaction records

  • Retention and cross-border transfer risks

For Teachers/Administrators

  • Access management vulnerabilities

  • Professional privacy exposure

  • Information accuracy concerns

  • System access challenges

Analysis of AI-specific risks

Analysis of AI-specific risks

The platform monitors content appropriateness and safety, potential algorithmic bias, decision-making transparency, and maintains boundaries between AI services and student data.

Security measures assessment

Security measures assessment

Technical Controls

  • HTTPS encryption for data in transit

  • Encryption at rest for stored data

  • Role-based access restrictions

  • Routine security evaluations and upgrades

  • Redundant backup infrastructure

Organizational Controls

  • Privacy documentation and processes

  • Designated Data Protection Officer

  • Incident response frameworks

  • Security training programs

Special category data handling

Special category data handling

Educational records receive FERPA guidelines protection. Users under 13 get enhanced protections in compliance with COPPA. Access restrictions include special handling procedures for any sensitive educational content.

Risk mitigation efforts

Risk mitigation efforts

Technical safeguards

Technical safeguards

Data Protection Methods

  • HTTPS encryption during transmission

  • Encryption at rest for stored data

  • Controlled API connections to service providers

  • Recurring security patches

  • Role-based, multi-factor authentication options

  • Session controls and timeout features

  • Comprehensive activity logging

Organizational methods

Organizational methods

Policy Framework

  • Detailed privacy documents

  • Ongoing team training

  • Emergency response procedures

  • Data management guidelines

Management Structure

  • Assigned Data Protection Officer

  • Security incident reporting pathways

  • Periodic policy revisions

  • Documented security procedures

Specific protections for children's data

Specific protections for children's data

COPPA Compliance:

  • Parental authorization requirements

  • Age-suitable privacy features

  • Limited information distribution

  • Strengthened security measures

Educational Privacy (FERPA):

  • School-based authorization systems

  • Minimal minor data collection

  • Strict access management

Data subject rights procedures

Data subject rights procedures

Users can submit access requests through a straightforward process with a 30-day response timeline.

AI-specific considerations

AI-specific considerations

Model training data sources and quality

Model training data sources and quality

The platform enforces strict data usage limitations, ensuring no student data is used for AI model training.

Algorithm transparency and explainability

Algorithm transparency and explainability

Transparency includes documented AI usage, interaction audits, teacher oversight of AI content, and explicit consent requirements for AI features.

Automated decision-making impacts

Automated decision-making impacts

The system avoids autonomous determinations entirely. All personalization is based on explicit inputs with mandatory teacher review and documented AI influence.

Bias monitoring and mitigation

Bias monitoring and mitigation

Regular audits assess content safety and detect bias. Teacher review processes and continuous monitoring protocols identify concerns.

Compliance Demonstration

Compliance Demonstration

Regulatory Compliance

Regulatory Compliance

The platform adheres to:

  • GDPR standards

  • FERPA guidelines

  • COPPA regulations

Data Sharing Agreements

Data Sharing Agreements

Third-party relationships remain limited to essential providers with data processing agreements. No commercial data sharing occurs, and compliance receives periodic review.

Document History

Document History

Date

Version

Change

April 14, 2026

2.0

Added missing AI sub-processors (Replicate, E2B, Exa); Updated hosting providers (Vercel, Fly.io, Supabase); Added third-party integrations; Added Circle SSO; Clarified encryption (HTTPS + at-rest, not E2E); Added geolocation and analytics data categories

December 18, 2024

1.0

Initial version