In previous posts, I walked through the process of deploying LLMs using vLLM and Ollama across various production environments, addressing challenges such as storage, scaling, and performance.
However, there was one thing I intentionally left out. security.
Security is one of the most overlooked aspects when designing AI systems. We get excited about running powerful models and optimizing performance, but we often assume “everything will be fine” on the security side.
For LLM and inference workloads, security is even more critical. These services consume significant resources, expose sensitive data, and, without proper protection, become easy targets for misuse, leaks, or cost explosions.
In my latest article, I explain how to secure an inference service using Authorino + Envoy, adding authentication layers that protect your API without complicating your pipeline.
Read the full article on Securing an inference service with Authorino
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