Traditional threat modeling assumes systems are deterministic. They follow rules, respect permissions, and behave predictably. Agentic AI breaks every one of those assumptions. Autonomous agents plan, remember, use tools, and take actions—and with that autonomy comes entirely new categories of risk. This course is the first to focus specifically on security for agentic systems. You'll learn how agents fail in practice, from memory poisoning to privilege escalation to cascading reasoning failures, and how to build controls that actually work in production.

This Course Offers

• A Complete Framework for Agentic AI Threat Modeling: Move beyond prompt injection and RAG leaks to understand system-level risks that emerge only in autonomous agents.
• Deep Analysis of Agent Failure Modes: Explore persistent memory corruption, unsafe tool invocation, reasoning drift, privilege escalation, and cascading failures across multi-step execution.
• Practical Artifacts and Templates: Work with agent reference architectures, threat surface maps, memory and tool security checklists, full agent threat model templates, and incident reconstruction frameworks.
• Real-World Mitigation Strategies: Design strict policy and oversight layers, detect privilege drift, trace failures across planning loops, and implement controls that actually work in production.

Why We Love This Course

1. It addresses a critical gap in AI security. Most AI security content focuses on prompt injection and model-level issues. This course tackles the harder problem: system-level security for autonomous agents.
2. It's deeply practical. Every concept is tied to artifacts, diagrams, templates, and exercises you can reuse in real projects.
3. It treats agentic AI as critical infrastructure. This isn't about demos and toy examples—it's about securing systems that take real-world actions.
4. It's built for practitioners. Security engineers, software architects, AI engineers—this course speaks your language and addresses your real challenges.

Agentic AI is being deployed faster than security models can evolve. Teams are shipping autonomous systems without understanding how they fail. This course gives you the frameworks to prevent those failures before they happen.

• Security engineers working on AI-driven or autonomous systems who need to understand agent-specific risks.
• Software architects designing agent-based or multi-tool workflows who must build security into system design.
• AI engineers building autonomous agents with memory and planning who need to prevent failures before they happen.
• Application security and cloud security professionals expanding their expertise into AI security.
• Technical leads and engineering managers responsible for AI risk and governance in their organizations.
• Anyone building or securing agentic AI systems who wants to stay ahead of emerging threats.

• Basic understanding of how large language models work at a conceptual level.
• Experience with software systems, APIs, or distributed architectures.
• Familiarity with security concepts such as permissions, attack surfaces, or threat modeling.
• Prior exposure to AI agents or automation workflows is helpful but not required.
• No advanced math or machine learning background required.

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