Defending Agentic AI: Securing MCP & Pipelines Mastery

About this course

This course contains the use of artificial intelligence.

We only charge a fee solely for the time invested in building this comprehensive curriculum.

The "Just Connect the MCP Server" Problem

The Model Context Protocol solves a real problem: it standardizes how AI agents discover and call tools, read resources, and use prompts. But that same standardization creates a new, specific attack surface. An agent that can call tools is now a potential path from untrusted text — a user message, a retrieved document, a poisoned tool description — straight into business APIs, databases, shell commands, and regulated records. "Just connect the MCP server" is how a chatbot demo becomes an unsafe automation layer with privileged access and no audit trail.

This course treats MCP security as what it actually is: a systems architecture problem. You don't secure an agent pipeline by trusting the model to behave. You secure it by putting a policy-enforcing gateway between every agent and every tool, validating everything at every boundary, classifying tool effects by risk, and requiring human approval before anything consequential happens. Every one of those principles gets built and tested here — not just described.

The Solution: 100 Labs, One Growing Defense Platform

Every lab follows the same Zero-Failure framework: a pre-flight check, a tar-based safety net, and a documented rollback. By Lab 10 you'll have a containerized, tested tool API with a repeatable security baseline check — a safe, working foundation before any of the harder architecture arrives.

What's InsideModules 1–2 build your workstation and MCP core server security: a documented threat model naming MCP-specific assets and risks, a concepts map distinguishing hosts, clients, servers, tools, and resources, typed Pydantic tool inputs, a published tool manifest, a static scanner that detects tool metadata poisoning b

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