Why ‘Verify’ Is the New Expertise in AI-Driven Internal Audit
As LLMs enter internal audit and digital forensics, the real differentiator is no longer whether you use AI — it is how you verify what it finds.
In AI-driven internal audit, the decisive skill is verification, not the model itself. LLMs can read the thousands of contracts, messages, and emails that humans never could, surfacing risk signals and summarizing them. But the moment an LLM is wrong, it is wrong plausibly — and a plausible false positive is the most dangerous outcome in an investigation.
Three pitfalls recur in the field: hallucination (inventing facts), bias (skewed training data working against specific people), and unexplainability (no reason given for a suspicion). Fraud findings end in discipline, investigation, or litigation; an unsupported suspicion collapses there.
The antidote is a single principle: AI finds, humans prove. Every alert must carry an original-source citation or be discarded. LLM judgments are cross-checked against rule-based detection and human review. And explainability must be preserved so the basis of every decision survives later scrutiny.
Digital forensics adds one more requirement — evidentiary integrity. Chain of custody must hold from collection to reporting, and the collection itself must be lawful. AI does not replace the auditor; the ability to handle AI in a verifiable way is the new expertise. Park Jae-hyun is a digital forensics and AI internal-audit expert who builds these methods into practice, including the LLM/AI audit-advisory system he designed, AI Audit Advisor.
글쓴이 · 박재현 (Park Jae-hyun)
LLM·AI 기반 내부감사 · 디지털 포렌식 전문가 · Ethic Code Engineer