This article is part of my MCP series. In the previous article, I covered how to deploy an MCP-based AI agent using Docker, Kubernetes, CI/CD, and observability.
Read the previous article: Productionizing an MCP-Based AI Agent with Docker, Kubernetes, CI/CD, and Observability
An MCP server can connect an AI application to databases, APIs, files, cloud platforms, and internal business systems.
That makes it useful, but it also creates risk.
A poorly secured tool could expose private data, modify the wrong resource, or allow one user to affect another user’s workload.
