DryRun Security

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Introduction

DryRun Security reviews code from an adversary’s perspective to help teams trust AI-written code. Its products include AI-native contextual SAST, SCA with dependency exploit validation, automated adversarial PR reviews, DeepScan repository reviews, custom code policies written in natural language, secrets detection that verifies which secrets are active, and IaC checks for Terraform, Kubernetes and CI/CD workflows.

A growing security context graph of your application informs reviews. The site says it analyses 550,000 pull requests a month across customers.

Roll DryRun Security out on one or two active repositories first and ask developers whether its pull request comments are useful or noisy. Natural-language policies are easy to write, so it is worth encoding a few of your own security rules, such as authorisation checks on admin routes, during the trial.

Features

1. Adversarial PR review: Before merge.
2. Contextual SAST: AI-native.
3. Exploit validation for SCA: Is the dependency really exploitable?
4. Natural-language policies: Write rules in English.
5. Secrets and IaC: Verify active secrets.

Pros

1. PR-native: Feedback where developers work.

Cons

1. Pricing not listed: Contact sales.

Usage and Links (Example)

1. Install the GitHub or GitLab app.
2. Write a few natural-language policies.
3. Review adversarial comments on PRs.

Resources:

• DryRun Security website

Pricing

Prices were not listed on the website when checked on 11 November 2026; contact the vendor for plans.

Recommended for?

1. Teams merging lots of AI-generated code.

Conclusion

DryRun Security turns security review into a PR-time habit.

Start with policies for your highest-risk code paths.

On CheckOps, DryRun Security is filed under the Security testing and Release & CI gates stages. Related tools listed here include Semgrep, Snyk, CodeRabbit, GitHub Advanced Security, Checkmarx and Aikido Security. A short trial on a real project, with your own code, data and pipeline, is usually the fastest way to choose between them, because fit matters more than feature lists. Facts here come from the official site, documentation and repository, checked on 11 November 2026.