AI Security Bootcamp-Guardrails,LLM Gateways,Observability

About this course

Complete AI Security Bootcamp

Artificial Intelligence is transforming every industry, and Large Language Models (LLMs) and Agentic AI are rapidly becoming the foundation of modern software. However, as organizations move AI applications from prototypes to production, security, reliability, and governance have become just as important as model performance.

This comprehensive bootcamp is designed to teach you how to build secure, observable, and production-ready AI applications using the latest tools, frameworks, and best practices adopted across the industry.

Unlike traditional Generative AI courses that focus only on prompting or building chatbots, this course dives deep into the engineering practices required to deploy AI systems safely in real-world environments. You'll learn how to protect LLM applications against prompt injection, jailbreak attacks, data leakage, hallucinations, unsafe outputs, and other common security risks while implementing enterprise-grade monitoring, evaluation, and governance.

The course begins by covering the fundamentals of building AI applications using LangChain and LangGraph, providing a strong foundation for developing intelligent workflows and multi-agent systems. From there, you'll explore the essential pillars of Production AI Security.

You'll learn AI observability using LangSmith and Pydantic Logfire, enabling you to trace, debug, monitor, and optimize AI applications running in production. You'll understand how to identify bottlenecks, inspect agent execution paths, monitor token usage, and troubleshoot failures efficiently.

Next, you'll master AI Guardrails using industry-leading frameworks including NVIDIA NeMo Guardrails, AWS Bedrock Guardrails, and Guardrails AI. You'll learn how to enforce safety policies, validate user inputs, moderate model outputs, implement content filtering,

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