Threat Modeling for Agentic AI: Attacks, Risks, Controls

Modern AI systems are no longer passive language models. They plan, remember, use tools, and act autonomously. And that changes everything about security.

Threat Modeling for Agentic AI is a deep, practical course dedicated to one critical reality: traditional threat modeling fails when applied to autonomous agents. This course teaches you how to identify, analyze, and control risks that emerge only in agentic systems - risks caused by memory poisoning, unsafe tool usage, reasoning drift, privilege escalation, and multi step autonomous execution. If you are building, reviewing, or securing AI agents, this course gives you the frameworks you cannot find in classical AppSec, cloud security, or LLM tutorials.

Why this course existsMost AI security content focuses on:Prompt injectionRAG data leaksModel hallucinations in isolationThis course focuses on what actually breaks real agentic systems:Persistent memory corruptionCascading reasoning failuresTool chains that trigger real world actionsAgents escalating their own privileges over timeYou will learn how agents fail as systems, not as single model calls. What makes this course differentThis is not a conceptual overview. This is a system level security course built around real agent architectures.

You will learn:How autonomy expands the attack surfaceWhy agent memory is a long term liabilityHow small hallucinations turn into multi step failuresWhere classical threat models completely miss agent specific risksEvery concept is tied to artifacts, diagrams, templates, and exercises you can reuse in real projects. What you will learnBy the end of the course, you will be able to:Threat model agentic systems end to end, not just individual componentsIdentify memory poisoning vectors and design integrity controlsAnalyze unsafe tool invocation and high risk capability exposureDetect privilege drift and unsafe delegation inside agent workflowsTrace cascading failures across planning loops and execution graphsDesign strict policy and oversight layers for autonomous agentsYou will not just understand the risks. You will know how to control them.

Course structure and learning approachThe course is structured as a progressive system analysis, moving from foundations to real failures. You will work with:Agent reference architecturesThreat surface mapsMemory and tool security checklistsFull agent threat model templatesIncident reconstruction frameworksEach module builds directly on the previous one, forming a complete mental model of agent security. Hands on and practical by designThroughout the course you will:Map threats across perception, reasoning, action, and update cyclesBreak down real agent failures step by stepIdentify root causes, escalation paths, and missed controlsDesign mitigations that actually work in production systemsThis course treats agentic AI as critical infrastructure, not demos.

Who this course is forThis course is ideal for:Security engineers working with AI driven systemsSoftware architects designing autonomous agentsAI engineers building multi tool or multi agent workflowsAppSec and cloud security professionals expanding into AITechnical leaders responsible for AI risk and governanceIf you already understand basic LLMs and want to move into serious agent architecture and security, this course is for you. Why you should start nowAgentic AI is being deployed faster than security models are evolving. Teams are shipping autonomous systems without understanding how they fail.

This course gives you the missing frameworks before those failures happen in your own systems. If you want to be ahead of the curve - not reacting to incidents, but preventing them - this is the course you have been waiting for. Start now and learn how to secure autonomous AI before it secures itself in the wrong way.