This document outlines security best practices for the AI Study Architect project. As an educational AI platform handling student data, we implement comprehensive security measures and follow industry standards.

• Student Personal Data: Names, emails, student IDs, grades
• Study Materials: Uploaded PDFs, notes, recordings, assignments
• AI Processing Data: Embeddings, cached responses containing user content
• Authentication Tokens: API keys, OAuth tokens, JWT secrets
• Database Dumps: Any exports containing user data

• Model Checkpoints: May contain traces of training data
• Logs: Must be sanitized of PII before sharing
• Configuration: Use environment variables, never hardcode
• Analytics: Aggregate only, never individual-level

• Code: Source files without embedded secrets
• Documentation: Public-facing docs, architecture diagrams
• Schemas: Database schemas, API contracts
• Tests: Unit tests with mocked data

• Run git status - review EVERY file
• Check for accidental inclusion of:
  • .env files or variants
  • Database files (*.db, *.sqlite)
  • User uploads (*.pdf, *.docx, media files)
  • Model files (*.pt, *.pkl, *.h5)
  • Log files containing user data
• Verify .gitignore is working: git check-ignore <filename>
• For new file types, update .gitignore FIRST

• All endpoints implement authentication requirements
• Rate limiting implemented
• Input validation implemented on all user data
• Uses ORM to reduce SQL injection risk
• Implements output sanitization to reduce XSS risk

• Ollama configured for optimal performance
• Implements policies to avoid user data in cloud AI calls
• Implements secure storage for embeddings
• Implements model output sanitization
• Implements prompt injection defense measures

• GDPR compliance measures:
  • Implements explicit consent mechanisms for data processing
  • Implements data export functionality
  • Implements right to deletion functionality
• FERPA compliance measures:
  • Implements educational records protection
  • Implements parent/student access controls
  • Implements audit trail for access

Note: This is a template for local development. Production values should never include localhost references.

# Database (Development Example)
DATABASE_URL=postgresql://user:password@localhost/ai_study_architect
REDIS_URL=redis://localhost:6379

# Authentication (Generate secure values for production)
JWT_SECRET=generate-a-secure-random-string
SESSION_SECRET=another-secure-random-string

# AI Services (Replace with actual keys)
OPENAI_API_KEY=your-key-here
ANTHROPIC_API_KEY=your-key-here

# Storage
UPLOAD_PATH=/secure/location/uploads
MAX_UPLOAD_SIZE=10485760

# Security (Production should use actual domains)
ENCRYPTION_KEY=generate-using-secrets-module
ALLOWED_ORIGINS=http://localhost:5173  # Dev only - use https://aistudyarchitect.com in production

If sensitive data is accidentally committed:

1. IMMEDIATELY:

   git reset --hard HEAD~1  # If not pushed

2. If Already Pushed:

   • Contact repository admin immediately
   • Use git filter-branch or BFG Repo-Cleaner
   • Rotate ALL exposed credentials
   • Document incident

3. Prevention:

   • Use pre-commit hooks
   • Regular security audits
   • Team security training

• Use .env.local for personal settings
• Never use production data in development
• Generate synthetic test data
• Use Docker volumes for databases

• Security-focused review checklist
• Check for hardcoded secrets
• Verify error messages don't leak info
• Ensure logging is appropriate

• Regular dependency audits: npm audit, pip-audit
• Keep dependencies updated
• Review new dependencies for security
• Use lock files for reproducibility

• Student data: Follows institutional policy
• Logs: Implements 90 days maximum retention
• Backups: Implements encryption at rest
• Deletion: Implements secure wiping procedures

• Implements role-based permissions (Student, Teacher, Admin)
• Implements principle of least privilege
• Implements regular access review procedures
• Implements multi-factor authentication for admins

• Implements logging for all data access
• Implements tracking for model interactions
• Implements API usage monitoring
• Implements regular security audit procedures

• Security Team: [email protected]
• Data Protection Officer: [email protected]
• Incident Response: [email protected]

Remember: When in doubt, keep it out (of git)!