AI-driven product security engineering: Ownership, speed, and impact

Product security engineering is not a new discipline, but measurable impact is what sets mature programs apart. This talk shares how a traditional security model was transformed into an AI-enabled, developer-centric capability that achieved a 50% reduction in security issues and measurable improvements in security scores. By correlating signals across tools such as Snyk, Gitleaks, Trivy, Netsparker, Sonar, and API security scanners, AI helped reduce noise, prioritize real risk, and accelerate remediation. It shows how AI enabled security engineers to ramp up quickly across diverse developer stacks, while targeted upskilling and a clear RACI model empowered developers to own fixes, allowing product security engineers to focus on architecture, threat modeling, and systemic risk at scale.