How MCP can revolutionize the way DevOps teams use AI

As for security, MCP agents are subject to all of the risks that come with any type of LLM-based technology. They have the potential to leak sensitive data because any resources that are available to an MCP server could become exposed to a third-party AI model. A potential solution is to avoid third-party models by hosting models locally (or on a server located behind a firewall) instead, but not all models support this approach, and it adds to MCP setup challenges. 

MCP servers could also potentially carry out actions that you don’t want them to perform, like deleting critical resources. To control for this risk, it’s important to apply a least-privilege approach to MCP server design and management by ensuring that they can only access the minimum resources necessary to support a target use case. The capabilities of MCP servers are limited to the level of security access available to users, so by restricting user privileges, admins can restrict MCP security risks.

MCP and the future of AI in DevOps

To be sure, MCP is not perfect. But it constitutes a huge leap forward in terms of how DevOps teams can leverage AI. It’s also a technology that’s here and now, and that DevOps engineers can start using today. Going forward, it’s likely that MCP will become as integral to DevOps as technologies like CI/CD.