Cybersecurity has standardized on global models. CVSS, EPSS, shared threat intelligence feeds. These tools represent real scientific progress and most security programs would be worse off without them. But they share a foundational assumption that doesn't hold up under scrutiny: that risk looks roughly the same across organizations.

It doesn't. An internet-facing payment processor and an air-gapped manufacturing floor share the same vulnerability databases but almost nothing else about how risk actually lands in their environments. Global scoring tells you where attacker pressure exists across the industry. It has no opinion on which of your assets matter, whether your controls are working, or which of the thousand alerts in your queue are worth acting on.

This talk is about what comes next. Local models are ML systems trained on an organization's own data: scanned assets, telemetry, remediation history, control posture. They don't replace global signals, they build on them. Global exploitation probability becomes a starting point, and local evidence updates it. The output is a risk signal tuned to your environment rather than the average of everyone else's.

We'll walk through where global heuristics consistently break down in practice, specifically in vulnerability prioritization, alert triage, and identity risk, and show how local models address each failure mode with concrete examples. We'll also get into the parts practitioners actually need to know: what data you need, how to frame the training problem, and how to tell whether your local model is actually better than what you're running today.

You'll leave with a framework for combining global and local inference, practical steps toward building and validating your own enterprise model, and a better answer to the question security teams are increasingly asking: why does this score never seem to reflect what's actually happening in my environment.