The VP of Engineering at a mid-sized SaaS company told me something last month that stuck with me. His team had grown their codebase by 340% in two years, but headcount in security had increased by exactly one person. “We’re drowning,” he said, gesturing at a dashboard showing 1,847 open vulnerability tickets. “Every sprint adds more surface area than we can possibly audit.”
He’s not alone. I’ve had nearly identical conversations with CTOs at three different companies in the past quarter. The math doesn’t work anymore. Development velocity has exploded — partly due to AI coding assistants, partly due to pressure to ship faster — but security teams are still operating with tools and workflows designed for a slower era. Something has to give, and increasingly, that something is machine learning.
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