An exposed AI endpoint is a financial and security risk. Without robust authentication and rate limiting, a single malicious actor can exhaust your GPU capacity or leak PII from your RAG systems. Beyond the endpoint, you must also consider network security for GPU clusters to isolate workloads on shared infrastructure.
Layered Security for AI Endpoints
1. Identity-Based Authentication (OIDC)
Ensure that every request is authenticated via OIDC. This allows you to track token consumption by user and provides a clear audit trail for compliance reviews.
2. Token-Aware Rate Limiting
Traditional rate limiting (requests per minute) is insufficient. You need to rate limit based on tokens per minute to prevent a few large prompts from monopolizing your inference fleet.
3. Abuse and Prompt Injection Prevention
Use a gateway layer to inspect incoming prompts for injection attacks or prohibited content before they ever reach your model.
Final Takeaway
Securing AI endpoints is about protecting both your data and your compute. By combining identity-based auth with token-aware rate limiting, you ensure that your AI features remain available, affordable, and secure for your legitimate users.
Need to harden your AI endpoints against abuse and unauthorized access? We help teams build secure API gateways, implement OIDC-based auth, and design token-aware rate limiting. Book a free infrastructure audit and we’ll review your API security path.
