How to Secure AI Agents in Production: What MCP Gets Right (and What It Doesn’t)
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. As you design multi-agent orchestration (LangGraph, CrewAI), MCP's security gaps demand a governance layer to prevent prompt-injection-driven privilege escalation and credential leaks. Deploy an AI Gateway with authentication, minimal tool scoping, and guardrails before exposing any agent to untrusted data or external actions.