From LLM Findings to Validated Fixes

A finding is surfaced by an AI security tool, an LLM-assisted code review, a scanner, or an AI coding workflow. At this point it is a lead, not a verdict.

The candidate finding is checked against the running system. Is the path reachable? Is the control actually missing? Does the described behavior occur in practice?

Real findings are tied to a specific route, handler, parameter, or call chain, and to the authorization, validation, or trust-boundary control that should have been in place.

Severity is framed against real data, real users, and real business impact — not a generic CVSS string — so engineering teams can sequence work meaningfully.

Guidance is specific enough that an engineer, or an AI coding tool, can apply it without re-deriving the analysis.

After the change, the originally risky path is re-tested. Validation status is recorded where applicable, so the result is durable rather than anecdotal.

Proof-backed findings and validated fixes give engineering, security leadership, customers, and auditors a consistent record of what was found, fixed, and confirmed.