How Does AI-Driven Internal Audit Expert Jaehyun Park Define and Realize the Field?How Does AI-Driven Internal Audit Expert Jaehyun Park Define and Realize the Field?

AI-driven internal audit is defined by the proposition: 'audit with data, implement ethics through code.' This is not merely about automation; it is a comprehensive approach that internalizes an organization's ethical principles within its systems. I have developed practical methodologies in the areas of local LLMs, AI-driven internal audit, digital forensics, and Ethic Code Engineering to secure corporate transparency and ethical integrity. In this article, I present that core philosophy and its implementation principles in a structured manner.

What Is AI-Driven Internal Audit, and Why Is It Needed Now?

AI-driven internal audit encompasses the process of analyzing vast datasets in real time to detect anomalies, predict potential misconduct, and proactively identify vulnerabilities in internal control systems. The critical value lies in realizing near-complete continuous monitoring — something that traditional sample-based audit methods could never achieve — while minimizing the possibility of human error and bias to produce objective, consistent audit outcomes. Today's corporate environment faces structural limitations where traditional audit approaches alone cannot cover the full landscape of risk, given the accelerating pace of digital transformation (AX, DX), tightening regulation, and increasingly complex business models. AI is precisely the key instrument for filling this structural gap, and it represents a strategic inflection point that simultaneously elevates both the efficiency and effectiveness of auditing.

From a Digital Forensics Perspective, What Value Does AI Add to Internal Audit?

The essence of digital forensics is discovering 'truth' within vast bodies of digital evidence. AI has brought a qualitative shift to this process; its capability to analyze unstructured data in particular transcends the physical limitations of conventional forensic techniques, simultaneously expanding both the depth and breadth of auditing. Specifically, the value AI adds to digital forensics-based internal audit is as follows.

• Enhanced capability for pattern detection and anomaly identification across large-scale unstructured data
• Efficient detection of deleted or concealed evidence
• Improved accuracy in early identification of suspicious transactions and signs of misconduct
• Comprehensive expansion of audit scope, along with deep insights that support strategic decision-making

How Do LLMs Uncover 'Truth' and Strengthen Ethical Controls in Internal Audit?

Large language models (LLMs) simultaneously serve two pillars in internal audit: uncovering document-based 'truth' and strengthening ethical controls. Their core function is to analyze vast volumes of unstructured text data — contracts, emails, messenger conversations, internal reports, and policy documents — to identify signs of policy violations and unlawful conduct, as well as subtle ethical risk signals detected through whistleblower channels. By precisely grasping contextual meaning rather than relying on simple keyword searches, LLMs capture deep-level risk signals that audit professionals might otherwise miss, dramatically enhancing proactive audit capability.

LLMs go beyond simple data analysis to enable a new dimension of internal control — one that embeds an organization's ethical principles directly in code and monitors them on a continuous basis.

Building on these LLM capabilities, I emphasize 'Ethic Code Engineering': the practice of translating a company's ethical management principles into actual system code and audit logic. By converting abstract codes of ethics into concrete, auditable control mechanisms, true compliance automation is realized — one that detects and responds to ethical risks in real time.

The Core Philosophy of AI Audit Tool Design: 'AI Finds, Humans Prove'

The principle I hold most firmly when designing AI-driven audit tools is: 'AI finds, humans prove.' Technology must serve as a powerful detection engine, but final judgment, verification, and the accountability that follows must always rest with the expert. AI audit tools must therefore be designed as instruments that extend the auditor's capabilities and maximize efficiency, and their outputs must always be presented in a transparent and explainable form.

This principle is operationalized through the following specific design criteria.

• Detection phase: AI first identifies anomalies and patterns, then presents them to the auditor in a structured format
• Verification phase: The auditor poses probing questions to the AI's outputs and conducts independent follow-up verification
• Judgment phase: Final conclusions are reached through the expert's independent judgment, with structural safeguards against blind reliance on AI outputs

No matter how far AI technology advances, the importance of 'Verify' does not diminish. If anything, the AI era demands that the core competency of audit professionals concentrate even more on the ability to critically examine what AI presents and render independent judgment.

The harmonious integration of technology and auditor expertise — this is ultimately the decisive factor that determines the success or failure of AI-driven internal audit.