How to secure your remote MCP server on Google Cloud

As enterprises increasingly adopt model context protocol (MCP) to extend capabilities of AI models to better integrate with external tools, databases, and APIs, it becomes even more important to ensure secure MCP deployment. 

MCP unlocks new capabilities for AI systems; it can also introduce new risks, such as tool poisoning, prompt injection, and dynamic tool manipulation. These can lead to data exfiltration, identity subversion and misuse of AI systems. 

Securing an MCP deployment begins with a strong security foundation. Here are five key MCP deployment risks you should be aware of, and how using a centralized proxy architecture on Google Cloud can help mitigate them. 

Top five MCP deployment risks you should know

While there are some broader risks unique to AI, these five are especially important to be aware of when designing MCP deployments:

1. Unauthorized tool exposure: A misconfigured MCP manifest can create a vulnerability that allows unauthorized individuals ...