Mozilla deployed Anthropic's Claude to audit Firefox's codebase and identified 271 security vulnerabilities, with the model assisting in both detection and remediation workflows. This represents a validated production deployment of AI-assisted code analysis at enterprise scale. The precedent matters operationally. Large codebases historically required manual security review by specialized teams—a bottleneck that scales poorly with code velocity. Mozilla's execution demonstrates that AI models can handle both vulnerability discovery and patch generation for established systems, reducing the resource intensity of security audits. This creates measurable ROI for organizations maintaining substantial C/C++ or systems-level codebases. For builders, this signals that security analysis tooling can shift from purely human-driven processes to hybrid workflows where AI handles initial triage and pattern matching. The 271-bug volume suggests the model didn't simply flag false positives—it caught real issues at scale. For operators, this means security review capacity no longer constrains shipping velocity in the same way. Expect security teams to adopt similar workflows as a standard operational practice rather than one-off experiment.