Security Reviews, IRL: Reviewing AI-Generated Code in Production
Join leaders from Mallory and DryRun Security to walk through how PR review actually works in an AI-first codebase where manual review doesn’t scale and traditional SAST mostly adds noise. We’ll read real pull requests together, show the logic and authorization flaws that slipped past initial review, and break down why contextual analysis caught what pattern matching tools missed.
What you’ll see:
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Jonathan Cran
Founder @ Mallory

Ken Johnson
CTO and Co-Founder @ DryRun Security
FAQs
Review and prioritize the highest-risk issues
Within a few hours you’ll get the results and if you want, one of our top appsec experts will review the top issues with you and a provide a practical path to remediation. That’s it, no strings attached.
Run DeepScan Agent on your codebase
You kick off the scan from the dashboard. We monitor progress and handle any issues should they come up (don’t worry, they won’t!).
Connect your repo
You perform a 5-minute install in the GitHub or GitLab app for your repo(s). We’ll walk through permissions and keep the process simple.
Meet with a DryRun Security expert
Short discovery call to confirm repo scope and what you want to learn (auth, business logic, secrets, or all three).
When should I use a DeepScan Agent review instead of a PR review?
Use it when you need broader coverage, for example onboarding a repo, preparing for an audit, after major refactors, before a release, orwhen developers introduce a new language.
Many teams run DeepScan on a cadence per production repo (monthly/quarterly), at key release checkpoints, or when risk changes, for example after big dependency updates or major architectural changes.
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