AI Red Teaming and Assurance (Self-Paced)

Self-paced online training with expert support

Getting AI deployed responsibly is the easy half. The hard half is proving it — to your leadership, to your auditors, to your customers. Learn to attack an AI system on purpose, then to assemble the evidence that it is fit to run.

$1,049 12 clock hours Live Support Start anytime

This course includes

  • 12 hours of project-based training from experts
  • Answers from real instructors
  • Retake for free within 1 year
  • Access to class recordings (30 days)
  • Verified digital certificate of completion

This is the self-paced version. For live instruction, enroll in AI Red Teaming and Assurance.

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) through Securing AI/ML Systems: Protecting the Intelligent Attack Surface (Self-Paced) and AI Security Monitoring and Incident Response (Self-Paced).

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This course has a prerequisite

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.

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