AI coding assistants have changed how fast teams ship code. They have also expanded the attack surface. Empirical research finds that roughly 30% of AI-generated code snippets studied in real GitHub projects contain at least one identifiable security weakness, and the mix is familiar: SQL injection, cross-site scripting, hard-coded credentials, weak cryptographic defaults and unsafe deserialization. AI has not invented new categories of bugs. It has amplified the ones already latent in the open-source ecosystem and in familiar coding patterns.
Pre-production scanning alone cannot close the gap. SAST generates thousands of findings without an execution context. Manifest-based SCA lists every declared dependency, whether or not it ever runs. AI-assisted remediation without production grounding is a plausible-sounding guess that may break a live call path. What AppSec teams need is runtime truth: The concrete, observed behavior of the application under real traffic, which inputs reach which sinks, which libraries are actually loaded and which AI-introduced flaws are reachable.
This guide walks AppSec practitioners through a three-step approach: establish runtime truth across the application portfolio, protect running software in-process while prioritizing the AI-generated backlog by reachability, and close the loop with a runtime-guided remediation feedback cycle so every fix is anchored in how the code actually behaves.