Agentic AI Security MCP, Least Privilege & Tool Hardening

What you'll learn

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Enforce least privilege RBAC across Model Context Protocol (MCP) tools and autonomous agent APIs.
Harden agentic tool interfaces to stop Indirect Prompt Injection and privilege escalation attacks.
Deploy an enterprise MCP security gateway with schema-level argument sanitization in Python.
Design human-in-the-loop approval workflows for high-risk autonomous AI tool executions
Requirements

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Basic proficiency in Python or TypeScript, along with a foundational understanding of REST APIs, JSON Schemas, and basic AI agent concepts (like function calling). No prior cybersecurity experience is required.
Description

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This course contains the use of artificial intelligence.
Your AI agents have the keys to your kingdom - and nobody has told them which doors they are allowed to open. Every day, organizations deploy LangChain, CrewAI, and AutoGen agents with full-scope API tokens, unauthenticated MCP servers, and unrestricted tool access. The breach is not a matter of if. It is a matter of when.
This course is for
- DevSecOps engineers tasked with securing AI agent deployments that the ML team built without security review
- Cloud security architects who need to extend zero-trust principles to non-deterministic agentic workflows
- ML platform engineers responsible for hardening MCP servers and tool interfaces before production launch
- Security team leads who need a defensible agentic AI security policy to present to the CISO this quarter
- Application security engineers who understand traditional IAM but need to map least-privilege to runtime agent behavior
What You Will Learn

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- Map every tool interface in your agentic system to a 4-tier risk matrix and generate workflow-scoped allow-lists that eliminate 80%+ of unnecessary access
- Architect multi-step agentic workflows with context isolation boundaries that prevent data leakage between execution stages
- Implement session-scoped privilege decay using time-bound JWT tokens so agent permissions shrink over the workflow, not expand
- Harden MCP (Model Context Protocol) servers from zero to production-grade with authentication, capability scoping, input validation, and rate limiting
- Defend against prompt injection through tool responses using output sanitization and untrusted data delimiters
- Apply the 5-Level Least Autonomy Framework to classify and constrain agent decision-making authority from full freedom to deterministic execution
- Deploy middleware-level autonomy ceilings that the LLM cannot override regardless of its reasoning
- Build and exploit a vulnerable AI agent in a hands-on lab, then harden it using every technique from the course
- Produce a complete Agentic AI Security Policy document covering tool allow-lists, MCP standards, workflow architecture, and autonomy ceilings
- Audit any existing agent deployment against a 38-point security checklist and identify critical vulnerabilities within 30 minutes
Requirements

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- Intermediate knowledge of cloud security concepts (IAM, RBAC, API authentication)
- Basic familiarity with AI agent frameworks (LangChain, CrewAI, or AutoGen) is helpful but not required
- Python 3.10+ and Docker installed for the hands-on lab (setup guide provided)
- An OpenAI API key or compatible LLM endpoint for the practical exercises
Final Project
Complete the 48-Hour Action Plan Worksheet to produce your organization's Agentic AI Security Policy. You will inventory all active agents and tool interfaces, classify each agent's autonomy level, generate tool allow-lists, harden your MCP servers against the 12-point checklist, and present a defensible security architecture to your stakeholders. This is not a theoretical exercise - it is a deliverable you will use in your job.
Who this course is for

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Security engineers, AI architects, DevOps teams, and software developers building autonomous agentic AI applications who need to lock down MCP tools, enforce least privilege, and prevent prompt injection exploits in production environments.
Homepage

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