The real challenge is not only mitigating risks, but embedding reflection into the architecture itself. Too often enterprise AI is treated as an engine to accelerate, without cognitive filters. If we don’t expose the traces of thought, every error inevitably becomes a headline.

Exceptional analysis by Angela Kheir, Yuan (Emily) Xue, and Danielle Gorman on a topic every board and enterprise leader should treat as non-negotiable: AI risk before deployment. As AI capabilities expand, the operational exposure of enterprises expands even faster. The examples discussed in this episode illustrate a critical reality: most failures don’t stem from model weakness — they stem from governance gaps, untested workflows, and insufficient human-in-the-loop safeguards. A valuable contribution to the global dialogue on responsible, scalable AI operations.

The Pak 'n' Save chlorine gas recipe is the perfect example of why Red Teaming needs to simulate creative misuse, not just standard errors. It's the catastrophic unintended consequence that erodes public trust instantly. we need people thinking like malicious actors and liability officers, not just data scientists. We need to stop building systems that treat every prompt as an instruction to execute.

Great discussion! Early detection of AI risks is essential for safe and scalable enterprise adoption

Excellent conversation!