Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 hours

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

This condensed hands-on session provides developers and technical leaders with a practical foundation in AI system security — from understanding the unique attack surfaces of LLMs and agents to applying effective guardrails, validation, and monitoring.

Participants explore key security principles across LLM pipelines, agent architectures, and Model Context Protocol (MCP) environments.

Through five focused labs, attendees learn how to detect vulnerabilities, prevent data leakage, and implement safe execution patterns for AI-driven workflows.

By the end of the session, participants will have a working understanding of common AI attack vectors, defensive design patterns, and secure deployment practices for agents and MCP-based systems.

What you will learn

Participants leave with an actionable framework for assessing AI application risk, implementing safeguards, and integrating secure development practices into their LLM and agent workflows

What you will need

Conceptual understanding of what LLMs are. No prior ML training experience required. No GPU required — all labs run on standard 4-vCPU GitHub Codespaces.

Topics Covered

The workshop combines rapid conceptual overviews with practical, short labs:

1. Lab 1 – Understanding AI Threat Surfaces

• Explore how AI systems differ from traditional apps: prompt injection, training data poisoning, model exfiltration, and output manipulation.

2. Lab 2 – Secure Prompt and Context Handling

• Implement techniques for input sanitization, instruction filtering, and chain-of-thought isolation in LLM and agent pipelines.

3. Lab 3 – Guardrails and Policy Enforcement

• Apply open-source guardrail frameworks (e.g., Guardrails.ai or LlamaGuard) to validate responses and prevent unsafe completions.

4. Lab 4 – Securing Agent Tool Use

• Configure tools and connectors with least-privilege access and safe error handling. Examine how to restrict and audit agent actions.

5. Lab 5 – Securing MCP Interactions

• Learn how to authenticate, authorize, and scope MCP server calls. Practice securing endpoints and preventing untrusted tool injection.