Red Team Blue Models

In this narrated edition of “Red Team, Blue Models: Adversarial Testing in an AI-First World,” we walk through what happens when “we did a red team” becomes a comforting slogan instead of real assurance. You’ll hear how AI-first systems create a new attack surface that extends far beyond jailbreak demos, into the messy intersection of models, prompts, plugins, data, and identity. We break down why adversarial testing has to focus on attacker goals, long-lived systems, and cross-layer behaviors if you want a risk picture that actually matches the stakes for your organization. This episode is based on my Wednesday “Headline” feature from Bare Metal Cyber Magazine.

From there, the discussion moves into the practical choices leaders have to make. We explore what a serious AI red team function looks like, how “blue models” can continuously simulate and detect abuse, and what it takes to integrate adversarial testing into AI delivery without strangling product velocity. You’ll get leadership-level patterns for governance, evidence, and metrics that go beyond counting jailbreaks and toward measuring real exposure. If you are signing off on AI features, building AI platforms, or advising boards on AI risk, this episode gives you language and mental models you can use in the rooms where those decisions get made.