Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
MCP server authentication means verifying the identity of every connecting client and tool caller, rather than relying on network location or an assumption of a trusted environment.
Securing an MCP server means enforcing real authentication and authorization, scoping credentials narrowly, validating input and output, and logging every tool call for later review.
MCP servers introduce concrete risks including weak authentication, over-broad credentials, malicious tool descriptions, unreviewed third-party code, and insufficient logging of tool calls.
MCP changes AI security by standardizing tool and resource access across models, which shifts risk from bespoke integration code to shared servers, schemas, and discovery mechanisms.
MCP security covers the controls that keep the Model Context Protocol's connections between AI clients, servers, and tools from becoming a path for unauthorized access, data leakage, or unsafe actions.
A practical, grouped checklist for evaluating whether an AI agent deployment has the identity, authorization, containment, and monitoring controls it actually needs before going to production.
Monitoring an AI agent in production means watching decisions and action patterns, not just uptime, since an agent can be fully available and still be doing something it shouldn't.
Data analysis agents that query and summarize sensitive datasets need row-level authorization and output review, because a technically correct query can still return data the requester was never allowed to see.
Customer service agents handle sensitive personal data and can take account actions directly, which makes authentication, data exposure, and social engineering resistance the core security concerns.
Research agents that gather and synthesize information from many sources need controls against source manipulation and confident misinformation, since their whole output is a judgment call about what to trust.
Coding agents need security controls around what they can execute and commit, because the output of a coding agent is code that will run with real permissions somewhere.
Browser-using AI agents face a distinct risk because every webpage they visit is untrusted, attacker-controllable content that can inject instructions directly into what the agent reads next.
