Secrets in model inference pipelines: Securing API keys, tokens, and model endpoints

In modern AI model inference pipelines, securing API keys, tokens, and model endpoints is crucial due to the complex architecture and potential vulnerabilities at various layers. These pipelines, unlike training pipelines, require real-time data handling and continuous availability, involving multiple layers such as compute infrastructure, inference runtimes, and serving layers. Secrets such as API keys and tokens are embedded across these layers, representing potential exposure points if not properly secured, as they manage authentication and authorization processes. Common security risks include hardcoded credentials, token exposure through logs, and unsecured model endpoints, making it vital to implement robust secrets management practices. This involves using secure storage mechanisms, managed identities, secret management platforms like HashiCorp Vault or AWS Secrets Manager, and implementing token rotation and monitoring for suspicious activity. Platforms like Doppler provide centralized secrets management, enabling secure deployment and operation of AI systems at scale, ensuring credentials never reside in plaintext configurations and are dynamically injected at runtime, with comprehensive audit trails and automatic updates when credentials rotate.