Three steps to detecting and blocking AI-generated attacks

Attackers are using AI to find and weaponize vulnerabilities faster than defenders can patch them. Once a vulnerability is disclosed, an exploit can be in production in as little as three days, while the average organization still takes up to 74 days to close it out. At the same time, AI coding assistants are shipping new code at a pace that traditional SAST, DAST and perimeter WAF tooling was never designed for.

This guide walks AppSec practitioners through a runtime-first approach: Instrument running applications for inside-out visibility, detect real exploit attempts as they execute and block entire attack classes structurally, without waiting for a signature, a patch or a rule update.

AppSec teams will learn how Contrast’s runtime security platform closes the gap that pre-production scanners leave open, why traditional WAF and SAST tooling miss AI-accelerated threats, and what a three-phase implementation looks like across production workloads.

What you can expect from this guide:

• Why traditional WAF, SAST and DAST tooling cannot keep up with AI-accelerated attacks, and how runtime instrumentation closes the gap.

• The specific attack surface that AI-generated code creates, including injection flaws, insecure dependencies, logic bypasses and exposed secrets.

• How runtime taint-tracing distinguishes real exploit attempts from scanner noise and delivers near-zero false-positive alerts to the SOC.

• How structural, class-level blocking stops zero-day attacks at the sink without waiting for a CVE, patch or rule update.
• A three-phase implementation checklist for AppSec teams deploying runtime protection across production workloads.