Why your email security architecture matters more than your AI model

AI has changed the economics of attack. Large language models let adversaries craft convincing, personalized campaigns at scale, collapsing the old tradeoff between volume and sophistication. The email security industry has responded with better models and richer signals. The complaints from security teams haven't moved: attacks still get through, false positives still can't be resolved without creating new blind spots, and coverage gaps still take weeks to close.

The problem isn't the quality of the models. It's the architecture underneath them.

On September 10, Josh Kamdjou (Co-Founder & CEO, Sublime Security) and Dmitri Alperovitch (co-founder, CrowdStrike; chairman, Silverado Policy Accelerator) make the case for why detection architecture, not AI capability, determines whether defenders can keep pace. They'll cover:

• Why centralized detection models impose structural limits on sensitivity, response time, and transparency that better models alone cannot fix
• How distributed detection gives AI agents the foundation to do genuine engineering work: writing detections, backtesting against real data, and closing gaps in hours rather than vendor update cycles
• What a coordinated team of specialized agents looks like in practice, and why it changes what a small security team can accomplish
• Why the same architectural shift applies beyond email, across endpoint, cloud, and SIEM

If you're evaluating how AI fits into your detection program, or trying to understand why switching vendors tends to produce the same categories of frustration, come ready to stress-test the argument.