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Your AI Is Not Special: Red Teaming Before You Become a Statistic

by Chase Peterson | at Minnebar20

Thankfully, AI features usually don't fail in fun, dramatic movie-villain ways.

They fail in boring, expensive, and embarrassing ways: bad outputs, bad decisions, unsafe actions, misplaced trust, and workflows that quietly do the wrong thing at scale.

The failures come from biased data, unsafe tool access, overreliance on bad outputs, weak approval flows, and a lack of monitoring once the system ships

In this session we'll look at common failure modes, how your model can be attacked, and why your internal Red Teaming needs to include the whole business. This is half cautionary tale, half lab, and half builder’s playbook.

Chase Peterson

Chase is an Adjunct Instructor and AI Faculty Fellow at Metro State University, where he teaches Data Mining Tools, Advanced Data Mining and Applied AI in Business. His journey began on the factory floor as a welder, evolved through systems integration and building management systems, and landed squarely in the world of cybersecurity, AI, and Industrial Controls. With an MBA and CISSP, Chase bridges the gap between industry reality and academic insight. He’s been featured on Minnesota Public Radio discussing the evolving role of AI in higher education, and has spoken at conferences like MWAIS, ICAIS, and IACIS. Known for his lighthearted and generally optimistic approach to serious tech, Chase explores the chaos in the algorithm.

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