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Introduction

This guide covers product security practice for teams building software in an AI world: using AI agents to find and fix vulnerabilities, and keeping their output trustworthy.

It’s written for product and application security engineers, and for the engineering leads who work with them.

How it’s organized

Section titled “How it’s organized”

The guide is split into chapters, each covering one part of the work:

  1. Finding vulnerabilities: AI-driven scanning and review, and separating real findings from noise.
  2. Context for review: what a model needs to know about your code and systems to review changes well.
  3. Prioritizing risk: seeing where risk concentrates so effort goes where it matters.
  4. Remediation: fixing issues with AI help, and benchmarking whether the fixes hold.