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You are starting a shift and the alert queue already has three AI related alerts with an AI triage score attached. Your job at this stage is to produce a fast, defensible starting position on each alert. That means a working classification, the minimum evidence you must verify in underlying telemetry, and a clear statement of what you are not yet sure about. This course treats AI as an analyst input, not an authority. You stay accountable for the disposition.
A useful way to think about this course is that you will build a repeatable mental model for AI system incidents. The model starts with a named risk category, ties it to the part of the system that could be failing, and ends with the evidence that can confirm or rule it out. We will move slowly now because the rest of the course depends on this architecture being consistent when the queue is noisy.
We will begin by practicing an initial triage move for each alert. Focus on what you would verify first and what evidence you need before you accept the AI suggested direction. Then we will map those choices to the OWASP Top 10 for LLM Applications 2026 categories so our labels mean the same thing across the team. Let’s make the first set of triage calls together.
OWASP Top 10 for LLM Applications 2026 is a community driven list of the ten most critical risk categories for applications that use large language models. It is useful in incident response because it gives you stable names for what went wrong, even when the symptoms look different. When your ticket says LLM02 or LLM06, everyone should immediately picture the same kind of failure and the same kind of evidence to look for.
Here are the ten categories we will use throughout the course, with their exact numbering and names.
This course covers all ten categories at an incident response level. You will learn how to spot the likely category during triage, what telemetry usually answers the key questions, and what containment and remediation commonly look like when the system includes an LLM and supporting components such as retrieval, tools, and memory. Next, we will anchor each category to the incident response touchpoints where you are expected to produce evidence. Use the map to connect label to workflow.