Skip to content

PRIVACY_MODE_PRD.md

Latest commit

 

History

History
295 lines (225 loc) · 10.1 KB

PRIVACY_MODE_PRD.md

File metadata and controls

295 lines (225 loc) · 10.1 KB
Raw

Privacy Mode Implementation - Product Requirements Document

Executive Summary

Privacy Mode is positioned as Summer's core differentiator and default operating mode. Based on the documentation created and Cursor's approach, this PRD outlines the technical requirements to deliver on our privacy promises to game developers.

Key Promise: "Your code is never used to train AI models. Period."

Problem Statement

Game developers work on highly proprietary projects where code represents competitive advantage. Current AI coding tools often use developer code for training, creating concerns about:

  • Intellectual property leakage to competitors
  • Proprietary algorithms being learned by AI models
  • Loss of competitive advantage through data sharing
  • Compliance issues with NDAs and client contracts

Solution Overview

Privacy Mode as the default operating mode for Summer, providing:

  1. Zero data retention after request completion
  2. Local-first processing where possible
  3. Transparent data flow with immediate deletion
  4. No training on user code, ever

Core Requirements

1. Data Flow Architecture

Current State Question: How does our current data flow work?

  • Where is code currently stored during processing?
  • How long is it retained?
  • Which services currently see user code?

Required Architecture:

User Code → Summer Client → Summer API → AI Provider → Response → User
                ↓
        Immediate deletion after response
        No persistent storage
        No training data extraction

Implementation Questions:

  • Do we need to modify our current API architecture?
  • How do we ensure immediate deletion across all services?
  • What changes are needed to our current AI provider integrations?

2. Local-First Processing

Requirement: Project intelligence and analysis should happen locally when possible.

Current State Questions:

  • What percentage of our current processing happens locally vs. cloud?
  • Which features currently require cloud processing?
  • What's the performance impact of moving more processing local?

Implementation Requirements:

  • Local AST parsing and code analysis
  • Local project indexing and search
  • Local pattern recognition for coding style
  • Only send code to cloud for AI generation/modification requests

Technical Questions:

  • What libraries/tools do we need for local processing?
  • How do we handle different programming languages locally?
  • What's the memory/CPU impact on user machines?
  • How do we sync local analysis with cloud AI context?

3. Zero Data Retention

Requirement: Code is deleted immediately after processing, with no exceptions.

Implementation Details:

  • No database storage of user code
  • No log files containing code snippets
  • No caching of code for performance
  • No "anonymized" data extraction
  • Conversation context cleared on session end

Technical Questions:

  • How do we modify our current logging to exclude code?
  • What changes needed to error reporting (Sentry, etc.)?
  • How do we handle conversation context without persistent storage?
  • What's the performance impact of no caching?

Database/Storage Requirements:

  • Audit all current data storage points
  • Implement automatic data purging
  • Add deletion confirmations/logging
  • Ensure no code in backup systems

4. AI Provider Integration

Current Providers (based on documentation):

  • Anthropic (Claude)
  • OpenAI (GPT)
  • Google (Gemini)

Requirements:

  • Zero data retention agreements with all providers
  • Code sent only during active user requests
  • No training on user data
  • Immediate deletion after response

Implementation Questions:

  • Do we have zero retention agreements in place?
  • How do we verify providers are deleting data?
  • What API flags/parameters ensure no training?
  • How do we audit provider compliance?

Technical Implementation:

  • Add privacy headers to all AI API requests
  • Implement request/response logging (without code content)
  • Add provider-specific privacy controls
  • Monitor for any training opt-out violations

5. Transparency and Verification

Requirements:

  • Users can see what data we have about them
  • Processing logs (without revealing code content)
  • Deletion confirmations
  • Third-party access logs

Implementation Questions:

  • How do we log processing without storing code?
  • What user-facing tools do we need for transparency?
  • How do we provide deletion confirmations?
  • What audit trails are needed for compliance?

Technical Architecture Changes

Client-Side Changes

Local Processing Engine:

  • Implement local code analysis (AST parsing, dependency mapping)
  • Local project intelligence and indexing
  • Local conversation context management
  • Encrypted communication with servers

Questions:

  • What's the size impact on the client application?
  • How do we handle updates to local processing models?
  • What platforms need different implementations?
  • How do we handle offline functionality?

Server-Side Changes

Stateless Processing:

  • Remove all persistent code storage
  • Implement immediate data purging
  • Stateless conversation handling
  • Enhanced encryption for data in transit

API Modifications:

  • Add privacy mode headers to all requests
  • Implement request-scoped data handling
  • Add automatic cleanup on request completion
  • Enhanced logging (without code content)

Questions:

  • How do we handle conversation context without storage?
  • What's the performance impact of stateless processing?
  • How do we scale without caching user data?
  • What monitoring do we need for privacy compliance?

Infrastructure Changes

Data Handling:

  • Audit all current data storage points
  • Implement automatic data purging
  • Remove code from all log files
  • Ensure encrypted data in transit and at rest

Compliance:

  • Regular privacy audits
  • Data deletion verification
  • Provider compliance monitoring
  • User data access tools

Implementation Phases

Phase 1: Foundation (Weeks 1-4)

  • Audit current data flow and storage
  • Implement basic immediate deletion
  • Remove code from logs and error reporting
  • Establish zero retention agreements with AI providers

Phase 2: Local Processing (Weeks 5-8)

  • Implement local code analysis engine
  • Move project intelligence to local processing
  • Implement local conversation context
  • Add encrypted client-server communication

Phase 3: Transparency Tools (Weeks 9-12)

  • Build user data access tools
  • Implement processing logs (without code content)
  • Add deletion confirmation system
  • Create privacy dashboard for users

Phase 4: Verification & Audit (Weeks 13-16)

  • Third-party privacy audit
  • Provider compliance verification
  • Performance optimization
  • Documentation and training

Success Metrics

Privacy Metrics:

  • Zero code retention (verified by audit)
  • 100% immediate deletion compliance
  • Zero training data extraction
  • User trust metrics (surveys)

Performance Metrics:

  • Local processing performance vs. current
  • Response time impact of privacy measures
  • Client resource usage
  • User satisfaction with privacy trade-offs

Compliance Metrics:

  • Provider compliance verification
  • Audit findings and resolution
  • User data access request handling
  • Deletion confirmation delivery

Risks and Mitigation

Technical Risks

Performance Impact:

  • Risk: Local processing may be slower
  • Mitigation: Optimize local algorithms, progressive enhancement

Scalability:

  • Risk: Stateless processing may impact scalability
  • Mitigation: Horizontal scaling, efficient resource management

Complexity:

  • Risk: Privacy measures add system complexity
  • Mitigation: Incremental implementation, thorough testing

Business Risks

Competitive Feature Gap:

  • Risk: Privacy measures may limit some features
  • Mitigation: Focus on core use cases, communicate trade-offs clearly

Development Timeline:

  • Risk: Privacy implementation may slow other features
  • Mitigation: Parallel development streams, prioritize core privacy

Open Questions for Engineering Team

Architecture Questions

  1. What's our current data retention timeline across all systems?
  2. Which services currently cache or store user code?
  3. How do we handle conversation context without persistent storage?
  4. What's the performance impact of local-first processing?

AI Provider Questions

  1. Do we have zero retention agreements with all current providers?
  2. What API parameters ensure no training on user data?
  3. How do we verify provider compliance with deletion?
  4. What happens if a provider changes their privacy policies?

Implementation Questions

  1. What local processing libraries/frameworks should we use?
  2. How do we handle different programming languages in local analysis?
  3. What's the client app size impact of local processing?
  4. How do we handle updates to local models?

Compliance Questions

  1. What audit trails do we need for privacy compliance?
  2. How do we handle user data access requests?
  3. What documentation is needed for third-party audits?
  4. How do we verify deletion across all systems?

User Experience Questions

  1. How do we communicate privacy benefits without being preachy?
  2. What transparency tools do users actually want?
  3. How do we handle the trade-offs (fresh conversations, etc.)?
  4. What privacy controls should be user-configurable?

Conclusion

Privacy Mode represents both our core value proposition and a significant technical undertaking. The implementation requires changes across our entire stack, from client-side processing to server architecture to AI provider integration.

The key to success is maintaining our privacy promises while delivering the AI assistance that makes Summer valuable. This means thoughtful trade-offs, excellent local processing, and absolute transparency about our data handling.

Recommendation: Start with Phase 1 (Foundation) immediately to establish the basic privacy infrastructure, then iterate quickly through the remaining phases. Privacy should be the foundation everything else builds on, not an afterthought.

This PRD should be reviewed with the desktop team, backend team, and AI integration team to validate technical feasibility and refine implementation approach.