What's the Production Risk Hiding in Your AI-Generated Code?
An audit of the operational risk currently shipped into your codebase by AI coding tools — security gaps, hidden dependencies, broken assumptions, and the cleanup the team can't see yet.
- A scored profile across 6 dimensions — see exactly where you're strong and where the gaps are.
- Your biggest opportunities, mapped to specific next moves.
- A personalized video walkthrough from Shawn (optional) — a real read on your results.
Most engineering teams have adopted AI coding assistants faster than any developer tool in the past decade — and most have done so without any structured way to detect the risk those assistants quietly ship into the codebase. The exposure rarely arrives as a single dramatic failure. It arrives as a credential committed to history that nobody noticed, a hallucinated dependency now sitting in the lockfile, a permission check loosened in a diff that looked reasonable, a test that passes green and asserts nothing, an architectural rule bypassed because the AI didn't know it existed. None of this is a reason to ban AI assistants — it's an argument for an honest audit of what's already there.
This free vibe-coding production-risk audit scores your codebase exposure across six risk dimensions and returns a clear profile in about five minutes. It's built from 27 years of technology leadership across Fortune 500 and growth-stage companies — the same lens a fractional CTO would bring to your first conversation. The framing is deliberately practical: not a maturity model, but the specific gaps an auditor, a customer security review, or a postmortem would surface if any of them came looking next quarter.
What this vibe-coding production-risk audit measures
Production risk from AI-generated code is a profile, not a single number. The audit scores six dimensions independently so you can see where the real exposure sits: Secret & Credential Exposure (have keys leaked into commits or AI tool history), Dependency Inflation & Hallucinations (is the package list trustworthy and verifiable), Authentication & Authorization Drift (has AI quietly weakened access controls), Test Coverage Mirage (are your tests verifying behavior or just decorating the build), Architecture Drift Risk (has AI silently bypassed layering, ownership, or boundary rules), and Audit Trail Visibility (can you tell what's AI-generated versus hand-written and trace it back). The final question maps the specific places where you suspect risk has already landed — so the result points directly at what to audit first.
Why production risk from AI-generated code matters right now
The risk profile of AI-assisted coding is fundamentally different from traditional engineering risk. AI assistants confidently produce code that looks correct, passes basic review, and ships into production — and then carries failure modes humans don't typically introduce: fabricated APIs and packages, plausible-but-wrong business logic, insecure defaults, tests that assert nothing meaningful, and credentials pasted into chat histories the engineer assumed were private. The exposure compounds quietly: customer security reviews, SOC 2 audits, regulator inquiries, and incident postmortems all eventually ask which code was AI-generated and what controls applied to it. Engineering organizations that can answer those questions move faster — they say yes to new AI tools sooner because they can stand behind every commit. Organizations that can't are carrying a contingent liability they cannot quantify.
What you get at the end
You'll see an overall production-risk score, a band that describes where you stand (from Exposed through Audit-Ready), a per-dimension breakdown showing which risks have accumulated the most, and a suspicion map of the specific places in your codebase to audit first. From there you can request a personalized video walkthrough — a short, recorded read on your specific results and what a fractional CTO engagement would do to surface and contain the risk without slowing your engineers down. No generic sales deck.
Frequently asked questions
What kind of production risk does AI-generated code introduce?
AI coding assistants introduce risk in patterns humans typically don't: hallucinated APIs and packages that pass shallow review, credentials pasted into chat histories, plausible-but-wrong business logic, tests that assert nothing meaningful, insecure defaults like disabled CSRF or permissive CORS, and quiet drift away from architectural rules the AI didn't know about. The risk rarely shows up as a single dramatic failure — it accumulates quietly until a customer security review, audit, or incident makes it visible.
How long does the audit take?
About five minutes. Seventeen scored questions across six risk dimensions, plus financial-context questions and a suspicion map of where you think the risk has already landed. Your progress auto-saves, so you can pause and resume without losing answers.
How is this different from a normal code-quality review?
A typical code review looks at the change in front of it. This audit looks at the accumulated exposure in your codebase from how AI assistants have been used over time: what's in commit history, what's in the lockfile, whether auth checks have drifted, whether tests verify behavior, whether architecture rules survived. It's the gap between reviewing the next PR and knowing what risk has already shipped.
Who should take this audit?
CTOs, VPs of Engineering, security leaders, and founders whose engineers are using AI coding assistants — and who want a defensible read on the production risk that's accumulated in the codebase before a customer security review, an audit, or an incident forces the question.
What if I score badly?
A low score isn't a verdict — it's an audit map. Most engineering organizations score in the lower bands right now because AI assistants outpaced the controls. The follow-up walkthrough turns the score into a sequenced plan: what to audit first, what to remediate in the next 90 days, and what can wait. Knowing the order matters more than the number.