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AI code audit
Audit AI-generated code before real users depend on it.
AI-generated code often looks complete while hiding production risks in authentication, authorization, data access, secrets, third-party integrations, and edge cases. Verity reviews the system like an attacker, operator, and senior maintainer would.
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When this fits
- You are about to launch with user data or payments
- You pasted errors into AI until the app worked
- Row-level security, API permissions, or admin routes feel uncertain
- The app uses generated database queries or generated webhook handlers
- You need investor, customer, or enterprise confidence
Outcomes
- Prioritized security findings
- Authn/authz review
- Secrets and environment review
- Input validation and injection risk review
- Webhook and payment flow review
- Dependency and deployment risk review
Deliverables
- Written security audit
- Risk severity and exploitability notes
- Fix-first remediation plan
- Validation checklist for launch
- Optional implementation support
Questions
Is this a penetration test?
It is a security and production-readiness review. For regulated or enterprise environments, it can prepare you for a formal pentest by fixing obvious gaps first.
Do you need repository access?
Yes, for a useful audit we need code access and enough context to understand the product, data model, deployment, and integrations. Read-only access is usually enough for the first pass.
Can you fix the findings too?
Yes. Most engagements start with the audit, then continue into focused remediation for the highest-risk areas.