This article outlines best practices for securing the Model Context Protocol (MCP), which links large language models to various tools and data. It provides actionable steps for protecting MCP servers, enforcing access restrictions, and implementing human oversight to minimize risks.

Model Communication Protocol (MCP) is emerging as a standardized method for integrating AI tools and language models, promising to enhance automation and modularity in enterprise applications. While MCP shows potential for streamlining connections between clients and external services, it still faces challenges in security, governance, and scalability before it can be fully embraced in production environments. Organizations are encouraged to explore MCP's capabilities while prioritizing best practices in security and observability.