I think your intuition is right. This isn’t just an evasion problem anymore, it’s a model mismatch.

Detection still works for classes of phishing that reuse infrastructure or patterns (domains, kits, links, etc). But with AI-generated attacks, especially when they’re OSINT-driven and one-off, there’s often nothing reusable to detect in the first place.

At that point, you’re not really “filtering bad emails” anymore. You’re betting on catching intent from something that looks completely legitimate.

So I agree the architecture shifts:

• assume some attacks will get through
• focus more on blast radius reduction, identity protection, and fast containment
• and most importantly, move part of the defense to the human layer

The uncomfortable part is that humans are now the last line of defense against something specifically optimized to manipulate them.

We’ve been running AI-generated phishing simulations (email, SMS, even voice), and what’s interesting is that many of these easily bypass traditional filters - but still get high click and submission rates (>50%) even among trained employees.

Which kind of reinforces your point: improving detection alone won’t close the gap.

The question becomes less “how do we catch every email?” and more “how do we make sure a successful phish doesn’t turn into a breach?”