Vibe Coding Security and Governance Maturity Assessment
A 25-question diagnostic across 4 domains that tests whether an organization governs AI-assisted software development: the rules and input protection, how AI-generated code is reviewed and tested, how the dependencies it...
Overview
A 25-question diagnostic across 4 domains that tests whether an organization governs AI-assisted software development: the rules and input protection, how AI-generated code is reviewed and tested, how the dependencies it suggests and the code it produces are secured, and whether the organization measures results and learns. Written for the CTO, engineering lead or AppSec team who needs to answer: are we governing AI-assisted code, or just shipping it faster?
Detailed Description
Vibe coding, using AI to generate code through natural language prompting, is changing how software is built. Developers produce more code faster, but that speed brings new risks: proprietary code and secrets sent to external AI services, plausible but wrong logic that passes a quick review, hallucinated or typosquatted packages, license contamination from training data, and technical debt that builds up quietly.
This assessment follows the order in which those risks have to be governed. It starts with Policy and Input Protection (the ground rules, approved tools, IP position, restrictions on security-critical code and controls on what goes into prompts), then Code Review and Quality Assurance (identifying AI code and reviewing and testing it with the right skepticism), then Supply Chain and Security Scanning (validating what AI suggests and scanning what it produces), and closes with Accountability and Continuous Improvement (who owns AI code, what the data shows and how standards evolve).
Each question is scored on whether the capability is documented, implemented and effective, so the results separate governance that exists on paper from governance that works, and name Paper Tigers, Tribal Knowledge and Measurement Gaps. Every score comes with specific guidance on what to fix next. DevSecOps and Shadow AI and AI Sprawl Governance are companion models.
Assessment Details
Audience
CTO / VP Engineering / Engineering Lead / AppSec and DevSecOps Lead. Secondary: software architects, code reviewers, legal and compliance.
Purpose
Shows whether AI-assisted development is governed or merely tolerated, exposes where code, secrets, vulnerabilities and legal risk slip through, and gives each gap a concrete next step and a measurable target.
Effort
Initial assessment: 60-90 minutes with an engineering lead and an application security representative, with input from legal. Evidence gathering: 1-2 weeks, including a sample of AI-assisted pull requests, scan results and dependency logs. Reassessment: 45-60 minutes.
Cadence
Quarterly, because AI coding tools change quickly, and within 30 days of adopting a new AI coding tool, a security incident involving AI-generated code or a major change to the development pipeline.
Framework Alignment
KPIs
- AI Code Vulnerability Density - Vulnerabilities per 1,000 lines of AI-generated code compared with human-written code. Target: equal or lower.
- Restricted-Area Detection Rate - Share of AI-generated changes in security-critical areas caught before merge. Target: 100%.
- Hallucinated Package Catch Rate - Share of non-existent AI-suggested packages caught before installation. Target: 100% in controlled tests.
- AI Code Review Rejection Rate - Share of AI-generated pull requests needing revision after review. Target: tracked and trending downward.
- Vulnerability Recurrence Rate - Share of previously found AI code vulnerability types that reappear. Target: declining every quarter.
Dependencies
Assessment Outline
(4 domains · 25 questions)-
1
1 Policy and Input Protection
AI-assisted development policy, approved tools and boundaries, IP position, security-critical restrictions, proprietary code, secrets and regulated data in prompts, retention and training awareness
- 1.1 AI-Assisted Development Policy 1 questions
- 1.2 Approved Tools and Boundaries 1 questions
- 1.3 IP and Code Ownership Position 1 questions
- 1.4 Security-Critical Code Restrictions 1 questions
- 1.5 Proprietary Code Exposure Controls 1 questions
- 1.6 Secrets and Credentials in Prompts 1 questions
- 1.7 Customer and Regulated Data in Prompts 1 questions
- 1.8 Model Training and Data Retention Awareness 1 questions
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2
2 Code Review and Quality Assurance
AI code identification, review rigor, reviewer pitfall training, testing standards, architectural consistency
- 2.1 AI-Generated Code Identification 1 questions
- 2.2 Review Rigor for AI Output 1 questions
- 2.3 Reviewer AI Pitfall Awareness 1 questions
- 2.4 Testing Standards for AI Code 1 questions
- 2.5 Architectural Consistency 1 questions
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3
3 Supply Chain and Security Scanning
Hallucinated packages, dependency vulnerability scanning, lockfiles and integrity, typosquatting, SAST and DAST coverage, AI-specific rule tuning, license scanning
- 3.1 Hallucinated Package Detection 1 questions
- 3.2 Vulnerability Scanning for AI-Suggested Dependencies 1 questions
- 3.3 Lockfile and Integrity Verification 1 questions
- 3.4 Typosquatting Prevention 1 questions
- 3.5 SAST and DAST for AI-Generated Code 1 questions
- 3.6 AI-Specific Vulnerability Tuning 1 questions
- 3.7 Open-Source License Scanning 1 questions
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4
4 Accountability and Continuous Improvement
Developer accountability, security feedback loop, AI versus human quality metrics, productivity versus technical debt, standards evolution
- 4.1 Developer Accountability 1 questions
- 4.2 Security Feedback Loop 1 questions
- 4.3 AI vs. Human Code Quality Metrics 1 questions
- 4.4 Productivity vs. Technical Debt Balance 1 questions
- 4.5 Standards Evolution 1 questions
At a Glance
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