Scaling Product Security with AI and Automation

Abstract

Ensuring strong product security is a fundamental part of building trustworthy and resilient software. However, conducting thorough security reviews especially in fast-moving, agile engineering teams can be challenging to scale effectively. Traditional review processes are often time-consuming, resource-intensive, and difficult to keep consistent across teams and projects. This article introduces a practical, staged framework designed to improve the product security review lifecycle by leveraging automation and generative AI. The focus is on two key areas: threat modelling and secure design validation both of which are critical in identifying potential vulnerabilities early in the development cycle. By automating repetitive tasks and enhancing human decision-making with AI-powered tools, teams can accelerate reviews without sacrificing depth or quality. Targeted at product security engineers and cybersecurity professionals, the framework addresses real-world pain points such as lack of scalability, inconsistent processes, and limited security resources. It offers actionable insights into how modern tools can be integrated into existing workflows to support faster, smarter, and more scalable security practices. Ultimately, this approach aims to bridge the gap between strong security standards and the need for development speed, helping organizations build more secure products without slowing down innovation.