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Defending Agentic AI Securing MCP & Pipelines Mastery

Name: Defending Agentic AI Securing MCP &amp; Pipelines Mastery
Author: Baturi

Defending Agentic AI Securing MCP & Pipelines Mastery

Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Defending Agentic AI Securing MCP & Pipelines Mastery, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 9/2026
Created by Bayt Al Hikmah
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 112 Lectures ( 26h 14m ) | Size: 1.2 GB

What you'll learn

⚡ Architect a hardened MCP gateway from scratch
⚡ Defend against the real MCP threat taxonomy
⚡ Enforce identity and least privilege
⚡ Build structured trust boundaries
⚡ Orchestrate autonomous pipelines safely
⚡ Instrument the system for real observability
⚡ Harden the supply chain and deployment
⚡ Govern data the way regulated AI systems must
⚡ Engineer for reliability
⚡ Deliver a capstone-grade Sovereign MCP Defense Platform

Requirements

❗ Knowledge: Basic Python (functions, running scripts) and basic terminal comfort. No prior MCP, agentic AI, or security experience required — Module 1 builds everything from a bare workstation up. No prior exposure to LangGraph, CrewAI, or agent orchestration frameworks needed — this course builds the state-machine, approval-gate, and policy patterns from first principles in plain Python, transferable to whatever framework you use in production. No prior Kubernetes or DevSecOps experience required — Module 7 builds those skills using manifests and a local-cluster-compatible design. Software (all free/open-source): Docker, Git, Python 3.12. Python packages installed via pip in Lab 3: FastAPI, Uvicorn, Pydantic, pytest,httpx — all free and open-source. kubectl (optional, for Module 7's Kubernetes manifests) — no cloud account required. No paid AI API required. The entire course builds MCP-style tool servers, gateways, and orchestration logic using deterministic Python — every attack, defense, and policy decision is fully reproducible without spending money on model calls. Hardware: 10GB+ free disk space, 4GB+ RAM sufficient for most modules (Docker containers for later labs). No cloud account, no production AI system, and no real sensitive data required — every lab uses synthetic tool manifests, synthetic documents, and self-contained attack fixtures you build yourself.

Description

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 before it ever reaches an agent, structured outputs, request IDs for traceability, and a working MCP gateway skeleton with tool allowlisting.
Module 3 builds real identity and authorization: static token auth evolving into OAuth-style scope modeling, Rego policy-as-code with default-deny logic, policy fixtures documenting both allow and deny cases, human-approval metadata tied to tool effect (read auto-approved, write and delete requiring a human), append-only audit logging, and rate limiting — culminating in an identity-aware gateway that checks who you are, what you're scoped for, and how much you've already asked for.
Module 4 is the heart of MCP-specific defense: a real prompt-injection corpus with labeled attack and benign cases, a testable local filter measured for actual precision (not vibes), output validation schemas that stop unexpected tool responses from becoming hidden instructions, PII detection and redaction, explicit context labeling (trusted system, trusted user, untrusted web, untrusted tool, restricted secret), a safe prompt envelope that visibly separates trusted tasks from untrusted context, and quarantine for high-risk tool output before it can become new agent context.
Module 5 builds controlled autonomy: explicit agent state modeling, deterministic (not hallucinated) planning, an approval gate that blocks risky plans until a human signs off, an executor that only acts on approved plans, idempotency keys preventing duplicate actions, retry budgets and dead-letter queues, multi-agent role boundaries (researcher vs. operator vs. auditor), and workflow checkpointing for safe recovery.
Modules 6–7 make the system observable and shippable: structured JSON logging, OpenTelemetry trace hooks, a security evaluation harness that turns "is our filter good?" into a measured accuracy number, incident severity classification, a written incident playbook, evidence export bundling, dependency inventories, SBOM planning, a container vulnerability scan hook, a signed-release policy, and Kubernetes manifests hardened with non-root security contexts and default-deny NetworkPolicy.
Module 8 governs data the way regulated AI actually requires: a data classification matrix (public through restricted), a controlled ingestion pipeline separating an unreviewed inbox from an approved shelf, retrieval limited strictly to approved data, retrieval authorization checking user clearance against document classification, a DPIA-style AI processing record, retention cleanup automation, and a compliance control map linking every technical control to a governance expectation.
Module 9 proves the platform survives real operational pressure: health/readiness contracts, resource requests and limits, horizontal scaling design, a chaos test proving unapproved risky plans fail safely, circuit breaker logic, multi-agent handoff logging, loop detection for agents stuck repeating the same tool call, and Service-Level Objectives that explicitly include audit completeness, not just uptime.
Module 10 closes with sovereignty and research discipline: a local-only model routing policy that maps data classification directly to model choice (confidential data never leaves the local model), an air-gap deployment checklist, a decentralized research source ledger distinguishing primary sources from commentary, a hash-verified research bundle, and a sovereign secrets-handling plan.
The Climax: Lab 100 — The Sovereign MCP Defense Platform
Lab 100 builds and validates the full capstone: passing tests, a successful hardened image build, a complete evidence bundle, a verifiable research bundle, a final architecture document, and a capstone runbook — all tagged as a defensible release. The required capstone demonstrations are concrete: a controlled gateway that only allows approved tools, typed validation at every boundary, prompt injection and tool poisoning defenses, identity and scope-based authorization, human approval for risky effects, governed retrieval over approved data only, and sovereign deployment assumptions documented end to end. This isn't a certificate exercise — it's a system you could walk a security review board through, control by control.
Why Enroll Now
The market signal here is precise and current: 2026 job postings increasingly name AI security engineer, AI platform architect, agentic AI architect, and LLMOps architect as distinct roles — and the strongest descriptions consistently ask for exactly this blend of MCP/agent protocol knowledge, cloud-native security engineering, identity, policy-as-code, and observability. This is one of the only courses building that entire stack from first principles, on free and open-source tooling, with no paid AI API required — a working, portfolio-ready defense platform you can point directly at your own agent infrastructure.

Who this course is for

⭐ The AI Platform Engineer Standing Up MCP Servers for the First Time You've connected an MCP server to a desktop client and it works — until you realize that "it works" and "it's safe to give this agent tool access to production systems" are completely different claims. This course builds the missing layer: a hardened gateway, identity-bound authorization, and policy-as-code, so your MCP deployment is production infrastructure, not a demo.
⭐ The AppSec Engineer Moving Into Agentic AI Security You know threat modeling, but "tool poisoning" and "excessive agency" are new vocabulary for a genuinely new attack surface. This course maps the real MCP-specific threat taxonomy — tool metadata poisoning, context boundary attacks, unauthorized tool discovery — onto concrete, testable defenses you build and validate yourself, using the same rigor you'd apply to any other production security boundary.
⭐ The AI Platform Architect Designing for Regulated, Sovereign Environments You're accountable for GDPR, EU AI Act, or DORA-adjacent obligations, and "the agent calls tools" isn't an acceptable answer when those tools touch regulated data. This course builds sovereignty and governance into the architecture from Module 1 — data classification, local-only model routing, an air-gap deployment checklist, and a compliance control map — culminating in a capstone you could genuinely defend to a review board.

Homepage

https://www.udemy.com/course/defending-agentic-ai-securing/?couponCode=KEEPLEARNING

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