How Do I Prepare AI-Generated Code for Technical Due Diligence?

Q: How do I prepare AI-generated code for technical due diligence?

Survive technical due diligence by performing a security audit, verifying IP ownership, clearing knowledge debt through documentation, and ensuring license compliance. Use the Clean Vibe methodology to make your AI-assisted codebase audit-ready.

Key Takeaways

VCs and PE firms now use AI-powered audit tools to scan your codebase.
Auditors look for "LLM-characteristic patterns" and security vulnerabilities.
IP Protection and Comprehension Debt are the biggest red flags.
A "Clean Vibe Audit" is your best defense against valuation slashes.

The New Reality of Due Diligence

Investors have adapted to the AI era faster than founders. During Series A or an acquisition, they no longer just check if the code "works." They deploy forensic tools to detect:

Synthetic Debt: Is the codebase a "house of cards" built without understanding?
IP Vacuum: Do you actually own the code, or is it in the public domain?
Security Risk: Did AI bake in 2-year-old vulnerabilities from its training data?

The 4-Step Preparation Checklist

1. The Security Hardening

AI code has a 45-46% vulnerability rate. Before the audit, run a deep security scan. Pay special attention to:

Input validation (AI often skips this).
JWT/Auth implementation (common AI hallucination area).
Hardcoded secrets and configuration.

2. The IP Purity Check

Identify your "Core IP" — the code that makes your business valuable. If this code is 100% AI-generated, you have no moat.

Action: Re-document the architectural designs for these components.
Action: Ensure human-led refactoring has been performed on all core logic.

3. Clear Knowledge Debt

Auditors will ask your lead engineers to explain complex parts of the system. If the answer is "the AI wrote it," the audit is failing.

Action: Conduct "Knowledge Transfer" sessions for all AI-generated modules.
Action: Create ADRs (Architectural Decision Records) retrospectively if needed.

4. License Audit

AI tools (especially when used with "Context Flood") often accidentally copy patterns from GPL-licensed code into proprietary projects.

Action: Run a license compliance scan to ensure no "GPL contamination."

The Statistics

$45K-$52K

average cost of AI code remediation

Source: Source 37

40%

probability of project failure during audit

Source: Prologue

How the Book Helps

Chapter 10: The Audit provides a complete simulation of a technical due diligence process. It includes:

The exact "Red Flags" auditors look for.
A "Severity Matrix" for AI-related technical debt.
A step-by-step remediation guide for high-debt projects.