How Mature Is Your Governance Over AI-Generated Code?
A scored profile across the disciplines that prevent vibe coding from sinking your codebase: review, attribution, policy, and accountability.
- 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.
Vibe coding — engineers shipping AI-generated code without rigorous review, attribution, or guardrails — has gone from a developer-Twitter joke to a board-level governance question in under a year. The risk isn't usually a dramatic failure; it's the quiet accumulation of hallucinated APIs, plausible-but-wrong business logic, fabricated dependencies, and credentials pasted into prompts that never get rotated. An honest governance assessment looks past the productivity claims at whether your engineering org can actually stand behind the code its AI assistants are producing.
This free vibe-coding governance assessment scores your organization across six disciplines and returns a clear maturity profile in about six 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, where governance is treated as the thing that lets you adopt AI assistants more aggressively, not less.
What the vibe-coding governance assessment measures
Governance over AI-generated code is not a single number — it's a profile. The assessment scores six dimensions independently so you can see exactly where you're strong and where the gaps carry the most risk: Policy & Standards (are the rules for using AI assistants written, current, and enforced), Code Review Discipline (do AI-generated pull requests get the rigor matched to their risk), Attribution & Audit Trail (can you tell which code is AI-generated and trace it back), Security & Secret Detection (does your tooling catch credentials, hallucinated dependencies, and AI-specific anti-patterns), Accountability & Ownership (does someone own the postmortem when AI-generated code causes an incident), and Training & Awareness (do your engineers actually understand AI failure modes). The final question maps the specific controls you still need to put in place, so the result points directly at what to build first.
Why governance over AI-generated code matters right now
Engineering teams have adopted AI assistants faster than any developer tool in modern memory — and most have done so without updating a single policy, review checklist, or onboarding document to match. The exposure shows up in three quiet patterns: secrets leaking into commits because nobody added pre-commit detection, fabricated APIs and dependencies slipping past shallow review because the diff "looked reasonable," and incidents where the postmortem stalls on the question of who owns code that an AI substantially wrote. None of this is a reason to ban AI assistants. It's an argument for the lightweight governance layer that lets the productivity gains stick without the failure modes compounding. Mature governance over AI-generated code turns into a genuine advantage: it lets you say yes to new AI tools faster than competitors who have to pause and figure out whether they're allowed, and it becomes an asset in security reviews, audits, and customer conversations.
What you get at the end
You'll see an overall vibe-coding governance score, a band that describes where you stand (from Unmanaged through Defensible & Mature), a per-dimension breakdown showing which disciplines need attention, and a map of the specific controls you still need to put in place. From there you can request a personalized video walkthrough — a short, recorded read on your specific results and what a fractional CTO engagement would prioritize to close your gaps without slowing your engineers down. No generic sales deck.
Frequently asked questions
What is vibe coding and why does it need governance?
"Vibe coding" describes engineers shipping AI-generated code with shallow review, often accepting plausible-looking suggestions without verifying the underlying logic, APIs, or security implications. It needs governance because AI assistants introduce failure modes humans don't — hallucinated functions, fabricated dependencies, credentials pasted into prompts, plausible-but-wrong business logic — and traditional code review wasn't designed to catch them.
How long does the assessment take?
About six minutes. It's 18 scored questions across six governance dimensions plus a final question that maps the specific controls you still need to put in place. Your progress auto-saves, so you can leave and resume without losing answers.
Does AI-coding governance slow engineers down?
Good governance does the opposite. The aim is to build guardrails that match the failure modes — review checklists, secret detection, attribution, ownership — so engineers can use AI assistants confidently instead of being told no by default. Mature governance shortens security reviews, prevents the incidents that actually slow teams down, and lets you adopt new AI tools more aggressively because you can stand behind every commit.
Who is this assessment for?
It's built for CTOs, VPs of Engineering, security leaders, and founders at mid-market and growth-stage companies whose engineers are already using AI coding assistants — and who want a clear-eyed read on whether their governance can keep up with the code their team is shipping.
What happens after I get my score?
You'll see a full governance profile with per-dimension scores and a map of the controls you still need. If you'd like, you can share a few details and receive a personalized video walkthrough explaining your results and what a fractional CTO would prioritize to close your highest-risk gaps without slowing your engineers down.