A finding from a scanner, a researcher or an AI tool is a claim. Validation tests it: can it be triggered, in your version and your configuration, and what can an attacker actually do with it? The answer may be a safe proof of concept, a code review or a check that the vulnerable code path is reachable.
It saves time on both sides. Teams stop chasing false alarms, and real problems get a clear severity based on what was shown to be possible. It matters even more with AI-generated reports, which can look convincing while describing a bug that does not exist.
Some flaws are real but not reachable in your setup, for example when the vulnerable feature is turned off. Others are worse than the score suggests, because of what the system holds.
Require a reproducible demonstration for important findings, run tests in a safe copy and not in production, record the result and mark false positives so that they are not raised again.