privatemode.ai Threat Profile
Security Model
What privatemode.ai Protects Against
| Threat | Protection Method |
|---|---|
| Cloud Provider Access | TEE hardware isolation prevents infrastructure access |
| Service Provider Access | End-to-end encryption with client-controlled keys |
| Data Logging | Architecturally impossible to log plaintext data |
| Model Extraction | Isolated execution prevents model theft |
| Side Channels | Timing randomization and memory isolation |
| Network Interception | TLS + TEE encryption provides dual protection |
Trust Assumptions
- Hardware manufacturers (Intel, AMD) implement TEEs correctly
- Cryptographic primitives remain secure
- Client-side attestation verification is performed
Compliance Framework
Data Protection
- GDPR Article 25: Privacy by design through TEE architecture
- GDPR Article 32: Technical measures via hardware encryption
- HIPAA: Eligible for processing protected health information
- CCPA: No data retention meets California privacy requirements
Certifications
- SOC 2 Type II (in progress)
- ISO 27001 compliance
- Regular third-party security audits
Residual Risks
Partially Mitigated
- Advanced side-channel attacks (research ongoing)
- Sophisticated timing analysis
- Hardware vulnerabilities (requires vendor patches)
Operational Considerations
- Requires proper client-side attestation verification
- Performance overhead of 5-10% for encryption
- Dependency on hardware TEE availability
Best Practices
- Always verify attestation before sending sensitive data
- Use application-level encryption for defense in depth
- Monitor service health and attestation status
- Rotate API keys regularly
- Implement rate limiting to prevent abuse
External Resources
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