# AI Red Teaming and Assurance (Self-Paced)

Canonical URL: <https://www.nobledesktop.com/classes/ai-red-teaming-and-assurance-self-paced>

## Overview

The capstone of the applied AI security path, and the course that joins testing an AI system to being able to justify running it. It pulls apart two ideas that get used interchangeably and should not be: red teaming is the act of trying to break, mislead or misuse a system, while assurance is the documented case that the system is trustworthy enough for the job it has been given. The engagement lifecycle follows — scoping the testing to the system's risk classification, setting objectives that yield something usable, writing the rules of engagement that keep an exercise from being mistaken for an incident, and handling team composition and deconfliction. Technique comes next: matching method to attack surface, and weighing expert-driven manual testing against automated approaches on the understanding that automation buys breadth and consistency while people bring depth and invention.

**Two things set it apart.** One is agentic AI, where the attack surface grows from what a system says into what a system does — the newest material in the course and the part buyers deploying agents are genuinely uneasy about. The other is the assurance half: most AI security training stops once problems have been found, whereas this course builds the assurance case as claim, argument and evidence, in a form an oversight body can actually audit, together with Test, Evaluation, Verification and Validation records, model and system cards, and the path from documentation to a risk-acceptance decision. The genuinely hard judgments get taught too — telling a red-team finding from an ordinary software bug, ranking by impact rather than technical difficulty, and spotting the moment a finding stops being a finding and becomes an incident. **There is no lab, by design:** the instructor built scenario-based practice activities in place of hands-on labs, including a prompt-assessment exercise where learners judge real example prompts from an engagement, so nothing needs provisioning and the procurement and oversight people in the audience can take it beside the testers. It sits at the top of a path that runs from [AI Security Fundamentals for the Cyber Workforce (Self-Paced)](https://www.nobledesktop.com/classes/ai-security-fundamentals-for-the-cyber-workforce-self-paced) through [Securing AI/ML Systems: Protecting the Intelligent Attack Surface (Self-Paced)](https://www.nobledesktop.com/classes/securing-ai-ml-systems-self-paced) and [AI Security Monitoring and Incident Response (Self-Paced)](https://www.nobledesktop.com/classes/ai-security-monitoring-and-incident-response-self-paced).

## What you'll learn

- Distinguish red teaming from adjacent assurance activities, and place it correctly in the AI assurance lifecycle
- Run a red-team engagement lifecycle from planning through reporting
- Scope testing to a system's risk classification, and set objectives that produce usable results
- Write rules of engagement, and handle team composition and deconfliction
- Match adversarial testing techniques to the attack surface they actually address
- Weigh manual expert-driven testing against automated and tool-assisted approaches
- Apply probing techniques to generative AI systems
- Tell a genuine red-team finding apart from an ordinary software bug
- Build Test, Evaluation, Verification and Validation records that support a risk decision
- Construct an assurance case as claim, argument and evidence
- Interpret model cards and system cards, and judge independent evaluation results
- Structure a findings report and prioritize by impact rather than technical complexity
- Manage remediation and retesting, and apply human oversight as a mitigation
- Recognize when a finding becomes an incident
- Extend testing and assurance to foundation models and to agentic, tool-using systems

## Prerequisites

This is an advanced capstone course. Participants should already understand AI and machine learning security fundamentals and be familiar with adversarial testing or assurance work.

## Pricing

**Tuition:** $1049