SOC 2 Compliance for AI Startups

Enterprise customers won't buy AI solutions without SOC 2 compliance. It's table stakes. But AI companies face unique compliance challenges: model training data contains PII, API keys grant broad access, and inference endpoints process sensitive data in real-time.

Security: implement access controls, encryption, and monitoring. Availability: ensure uptime SLAs and disaster recovery. Processing Integrity: validate that AI outputs are accurate and complete. Confidentiality: encrypt sensitive data at rest and in transit. Privacy: handle personal data according to your privacy policy.

Model access: restrict who can fine-tune, deploy, or query models. Training data: document data sources, implement retention policies, and ensure deletion capabilities. Inference logging: log all model inputs/outputs for audit trails. Bias monitoring: implement fairness metrics and regular audits.

Start 6 months before your target audit date. Implement SSO + MFA for all systems. Encrypt all data at rest (AES-256) and in transit (TLS 1.3). Set up audit logging for every data access. Document your data flow from ingestion to inference. Run a readiness assessment 3 months before the audit.