Model Context Protocol systems make it possible for AI agents to connect with tools, data sources, and operational workflows, but production deployment introduces serious security and reliability challenges. Misconfigured permissions, untrusted servers, leaked credentials, weak validation, and poor observability can turn a useful agent integration into a significant business risk. This practical course teaches you how to secure and operate MCP systems with production-grade discipline. You will learn the MCP architecture and trust boundaries, identify threats across clients, servers, tools, and data flows, design least-privilege authorization, protect secrets and credentials, validate tool inputs and outputs, and establish controls for untrusted or third-party MCP servers. The course also covers secure deployment patterns, audit logging, monitoring, incident response, version management, and operational testing. The course is designed for AI engineers, platform engineers, security practitioners, backend developers, and technical leads responsible for deploying agent systems beyond the prototype stage. Familiarity with APIs, authentication, and basic cloud or software operations will help, but the lessons focus on practical production decisions rather than specialized security theory. By the end, you will be able to evaluate MCP security risks, define defensible controls, build an operational readiness checklist, and respond to failures with clear evidence and containment steps. Move from experimental agent integrations to MCP systems that are secure, observable, and ready for real-world operation.