AI Red Teaming

Break AI Systems Responsibly, So Others Cannot Break Them Maliciously

As AI systems spread into critical work, organizations urgently need people who can stress-test them, find their failure modes, and document the risks. That discipline is AI red teaming, and this course gives you a structured, professional foundation in it.

You will learn how to think adversarially about language models and how to turn that thinking into rigorous, repeatable evaluation. The course covers:

• The principles and ethics of responsible red teaming
• Categories of model failure: safety, bias, robustness, and reliability
• Designing structured adversarial test cases and evaluation suites
• Writing clear, professional red-teaming evaluation reports
• Applying the NIST AI Risk Management Framework to organize and communicate findings
• The Arabic edge: probing model behavior in Arabic, dialects, and code-switching, where many vulnerabilities go untested

What makes this course distinctive is its emphasis on Arabic-language testing. Most red-teaming work happens in English, leaving a real gap in how models behave in Arabic. You will learn to design tests that surface these blind spots, a skill that positions you at the front of an emerging specialization.

The course balances hands-on testing with the documentation and framework knowledge that make your work credible to organizations. It is ideal for language professionals, QA specialists, and anyone wanting to move into AI safety and evaluation.

Step into one of the most future-facing roles in the AI industry, with a focus the market badly needs.