AI Governance & Ethics
AI Governance / Ethics
What is AI governance?
- A management framework for using AI safely and fairly
- Setting principles, policies, and controls
- Regulatory compliance and trust
Once AI governance is in place, one thing disappears from the organization: the moment when a staff member decides alone, by gut feel, "Can I put this material into AI?" Which tools may be used for which tasks, who verifies the output, and who takes responsibility when something goes wrong are all written into documents and systems. It is a mechanism that shifts the burden of judgment from individuals to the organization, so it works less as a regulation that slows you down than as a foundation that lets you handle the speed.
Design usually starts by tiering risk. The EU AI Act, which took effect in 2024, sorts AI systems into four tiers (prohibited, high-risk, limited-risk, and minimal-risk) and imposes different obligations on each. Uses that directly affect people's rights, such as hiring screens or credit scoring, are classed as high-risk and carry duties for record-keeping, explainability, and final human intervention. Korean companies borrow this framework too, adjusting the strength of controls by use case instead of applying the same controls to every AI. Where an information security policy asks whether data left the company, AI governance goes further and asks who verifies and takes responsibility for what the model produces.
The most common failure in practice is building a ban list without offering alternatives. Even if company accounts are blocked, the workload stays the same, so employees move to personal phones and personal email. As Shadow AI use that the organization doesn't even know about spreads, it leaves no logs and no trace, and leaks only surface after an incident. The opposite failure is just as common: drafting an ethics statement, posting it on the website, and stopping there. A statement is not a control, only its preamble. Without approval procedures, usage logs, and review cycles, it is no more than a document to show auditors.
John KimInstructor John Kim, author of B2B Marketing Design and CEO of Nanum Consulting
Ara LimExpert in global leadership, AI, and business communicationThis field took shape as bias, safety, and privacy concerns emerged with the spread of AI. Its importance is growing with regulations such as the EU AI Act and the corporate Responsible AI movement.
Consider a hypothetical case. Company A is a medical device maker with 740 employees and KRW 230 billion (≈ US$172,078,400) in annual revenue. A security check flagged signs that a developer had pasted part of the product-control source code into a chatbot on a personal account. On investigation, employees using external AI services without company approval turned out to be spread across every department, and the CEO ordered a working framework to be in place within three months.
- When the head of information security pulled four weeks of proxy logs, attempts to reach external AI averaged 310 a day, and 68% of them came from the development and planning departments.
- The CISO and the head of legal sorted work into three tiers: work involving customer personal data and design drawings is banned, summarizing internal documents is allowed only on approved accounts, and researching public information is open to everyone.
- "If you only tell people not to use it, they'll just use it on their phones," said the head of development. So the company first opened 120 enterprise-account seats and cut the list of prohibited items down to a single page.
- The HR team ran six 1-hour sessions and showed three anonymized internal cases that had nearly turned into real leaks, exactly as they happened.
- Six months later, attempts to access AI on personal accounts dropped from 310 a day to 22, approved-account usage reached 84% of registered users, and detected entries of sensitive information fell to zero per quarter.