DryRun has been a strong force multiplier for our AppSec process. It analyzes changes in context (not just pattern matching), flags issues early in pull requests, and delivers feedback in a way engineers can act on quickly. We’ve seen fewer false positives vs. legacy SAST approaches, which makes it easier to build trust with dev teams.

DryRun Security runs and provides feedback where we do our work: GitHub. Feedback is provided quickly within the context of the Pull request. This helps our team mitigate vulnerabilities before they are deployed.Vulnerabilities are reported in a context-aware manner, which reduces the number of false positives.

We use traditional SAST tools, but they mostly depend on rule-based static analysis. DryRun Security, by contrast, focuses on understanding code intent and logical flow, which makes it effective at finding authorization flaws, broken object-level authorization, insecure direct object reference, and insecure business logic. As AI assistants such as Cursor or ChatGPT-based tools become more widely adopted, we face new risks from AI-authored code. DryRun Security helps us focus specifically on the logic flaws that can show up in AI-generated code snippets—issues that traditional scanners often miss.