Code-as-Auditor: Executable Compliance Reasoning via Regulation-to-Code
Source
Jisoo Kim, Taeyoon Kwack, Jinwoo Jang, Woo Kyung Kim, Honguk Woo
Author
Jisoo Kim, Taeyoon Kwack, Jinwoo Jang, Woo Kyung Kim, Honguk Woo
Date
Key takeaways · AI-distilled
Converts regulations into executable decision trees and checklists, encoding legal conditions as interpretable code rather than free-text prompts.
At inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition →, each checklist item expands into factual and counterfactual questions so the model reasons against case-specific evidence, not just static rules.
A self-verification loop checks the generated code's logical consistency, improving traceability of how each compliance decision was reached.
In tests on privacy and data-protection scenarios, the framework delivered more accurate, evidence-backed compliance evaluations than ungrounded LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → outputs, the authors report.
Terms in this piece · Glossary
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
Grounds LLM compliance reasoning in executable, auditable logic rather than free-text judgment, improving traceability for automated regulatory review.
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