We are not ready for what AI has done to red teaming

Publisher excerpt From CSO Online

Something shifted in offensive security over the last eighteen months that most organizations haven’t fully absorbed yet. It isn’t that attackers got smarter. It’s that the economics of attack changed entirely — and those of us who run red teams have had a front-row seat to the transformation. The conversation about AI in cybersecurity tends to fixate on defense: better anomaly detection, faster triage, and AI-assisted SOC analysts. That framing misses the more urgent story. The attack side of this ledger has already been rewritten. A small, well-equipped offensive team using frontier AI models today can outperform what was possible with a much larger traditional operation just two years ago, by a margin that should unsettle every security leader reading this. Through our work on the front lines of offensive security, the consensus is clear: if you are not augmenting your operations with AI, you are already at a disadvantage. Before addressing the operational shift, we must clarify a fundamental distinction that is often conflated in industry discussions: AI red teaming: This involves probing Large Language Models (LLMs) for vulnerabilities, testing agentic pipelines, and identifying prompt-injection vectors or unintended behaviors. Red teaming with AI: Using AI as an operator’s tool to execute traditional offensive operations. It is this second category, Red Teaming with AI, where the most immediate, drastic, and dangerous transformation is occurring. The staggering…

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