AI API Security Best Practices for Developers
AI API security is different from traditional API security. LLM keys control access to expensive compute, and a leaked key can cost thousands in minutes. Here are the essential security practices for AI API usage.
1. API Key Management
- Never hardcode keys — Use environment variables or a secrets manager. Never commit keys to git.
- Use a gateway key — Instead of giving each developer direct provider keys, issue gateway keys with spend limits through AI24X.
- Rotate regularly — Rotate API keys every 30-90 days. AI24X console supports key rotation with zero downtime.
- Revoke immediately — If a key is exposed, revoke it in one place rather than hunting through six repos.
2. Access Control
- IP whitelisting — Restrict API keys to specific IP ranges or VPCs.
- Rate limiting — Set per-key rate limits to prevent abuse and control costs.
- Spend caps — Set monthly spend limits on each API key. AI24X supports configurable caps.
3. Data Privacy
- Never send sensitive data — Don't send PII, credentials, or proprietary code to LLM APIs.
- Use BYOK for sensitive workloads — With AI24X BYOK, your data goes through your own provider keys, not shared infrastructure.
- Check data retention policies — AI24X does not store prompt or response data beyond what's needed for billing.
4. Secure Integration Patterns
- Server-side only — Never call AI APIs directly from mobile apps or browsers. Use a backend proxy.
- Input validation — Sanitize user inputs before sending to LLMs to prevent prompt injection.
- Output validation — Validate LLM outputs before displaying to users or using in business logic.
5. Monitoring and Auditing
- Log all API calls — Track which key made which request, to which model, at what cost.
- Set up alerts — Get notified when usage spikes or approaches spend caps.
- Review regularly — Weekly review of API usage patterns and security incidents.