How to Secure AI Agents in Production: What MCP Gets Right (and What It Doesn’t)
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Securing AI agents in production with MCP is directly on-topic and practically valuable.
Summary
MCP standardizes agent-tool communication but omits authentication, access control, observability, and guardrails, forcing teams to implement a separate AI Gateway for governance. The 'lethal trifecta'—private data, untrusted input (e.g., GitHub issues), and external actions (e.g., Slack)—enables prompt injection via tool outputs, tool permission creep, and sequence attacks. Production agents require this gateway layer to enforce scoped permissions and input/output filtering, as MCP alone cannot prevent data exfiltration.