Security teams aren’t failing. Their systems are.

Across our discussions with AI leaders and Heads of Security, the pattern looks the same. As AI systems are rolled out, security teams are pulled in for pre-production reviews. The intent is right. The tools and processes are not.

Most of these reviews are static. They evaluate architecture diagrams, access scopes and intended behaviour. But with AI systems, what matters is how they act at runtime, under changing context, across real interactions.

Not surprisingly, even when reviews are detailed, failures still happen. Not because the review was careless, but because AI systems fail in ways that can’t be fully predicted upfront. The behaviour emerges only once the system is live.

The result is a bad outcome for everyone. Security teams look ineffective. Builders feel slowed down. Leadership loses confidence in the process. Over time, teams start bypassing security reviews altogether, not out of negligence, but to keep up with the pressure to keep shipping.

This is not a people problem

This is not a people problem. It’s an operating model problem.

If AI is evaluated only before deployment, security will always be late. The only viable path forward is runtime evaluation and control, where every interaction is assessed right before it leads to an action.

Organizations that recognize this shift early won’t just reduce risk. They’ll unlock more automation, move faster and extract real business value from AI while others remain stuck choosing between speed and safety.