Securing GenAI Systems: From Prompts to Autonomous Agents

About this course

Generative AI has changed how software is built — but it has also introduced entirely new security failures that traditional AppSec and cloud security models were never designed to handle.

This course is a deep, hands-on journey into the real security risks of modern GenAI systems, from prompt injection and RAG poisoning to tool abuse and autonomous agent failures. It is designed for software engineers, security engineers, architects, and AI practitioners who need to move beyond theory and understand how GenAI systems actually fail in production — and how to secure them properly.

Unlike high-level AI safety courses, this program is practical, adversarial, and systems-focused. You’ll break real GenAI workflows, observe emergent failures, and then implement concrete defenses using industry-aligned patterns.

By the end of this course, you won’t just understand GenAI security — you’ll know how to design, test, and govern AI systems safely at scale.

What You’ll LearnCore Concepts

Why GenAI security is fundamentally different from traditional AppSec

How non-determinism breaks existing security assumptions

Where trust boundaries actually exist in AI systems

Why “prompt security” alone is insufficient

Hands-On SkillsExploit prompt injection and instruction hierarchy failures

Poison RAG pipelines and observe real-world impact

Abuse tool calling and function execution

Trigger unintended behavior in multi-agent systems

Implement real mitigations using policies, constraints, and governance

Defensive Architecture

Secure RAG design patterns

Tool and function authorization models

Agent guardrails

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