AI-powered security research is tearing open decades of latent bugs across the entire stack. Volume isn't a trend. It's a step function.
Nearly a third of exploited vulnerabilities are weaponized on disclosure day. If your patch cycle is monthly, you're not managing risk. You're documenting exposure after the fact.
AI closes the bug-to-exploit gap. When exploitation is that cheap, every CVE with conditions met is effectively in the wild. Offense scales with compute. Defense scales with headcount.
CVE data quality is degrading. NVD can't keep up. AI-generated reports are flooding the ecosystem. The scores you rely on are built on noisier data every quarter.
Pre-AI era vulnerability management programs can't keep up. That world ended.
Your scanner says it's there. But is it actually exploitable in your environment, with your configuration, your deployment? That's what your team investigates manually, finding by finding. Manual triage was already failing. The post-AI era made it undeniable.
Risk scoring requires your environment: topology, identity chains, permissions, what's downstream. Analysts assembled this manually, tool by tool. That bottleneck is now untenable.
Remediation has options beyond patching: disable a feature, tighten a security group, change a configuration. Knowing which applies requires understanding the specific conditions making it exploitable. That intelligence didn't exist at scale. Now it has to.