Vibe-Coded Evidence Evaluation Breaks in 12 Places
Generative AI makes it easy to build quick prompts and scripts for audit evidence evaluation. For recurring control testing, that approach creates hidden costs: prompt sprawl, fragile workflows, inconsistent output, limited auditability, and insecure data handling. This report maps the 12 places it breaks and compares it with a purpose-built evidence-evaluation system.
Why We Wrote This Report on Vibe-Coded Evidence Evaluation
A Demo Is Not a Control Test
A prompt that works on five files says little about thousands of records, every audit period, and an external auditor's review. We wrote this to show where the gap opens.
Hidden Costs Add Up
Token spend, model drift, prompt maintenance, and security reviews rarely show up in the first proof of concept. They grow with every framework and control you add.
Build Versus Buy Is the Wrong Frame
The real choice sits between a collection of AI experiments and an operating evidence-evaluation system. The report lays out what that system requires.
Why You'll Want to Read This Report
Pressure-Test Your Current Approach
Use the 12 failure points as a checklist against any internal scripts or prompt libraries your team already runs.
See a Side-by-Side Comparison
A capability table contrasts lightweight internal coding with the Vero AI platform across token efficiency, model strategy, security, and workpapers.
Know Where Vibe Coding Still Fits
The report closes with low-risk uses worth keeping, so experimentation continues without putting audit conclusions at risk.
Questions Compliance Leaders Ask About Vibe-Coded Evidence Evaluation
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