亞洲 AI 法規與資安：新加坡治理框架、PDPA 與企業該守的合規重點

Many companies, when introducing AI, only think about 'what can be done', but rarely ask 'can we do this' first. When incidents occur - data breaches, misuse of personal data, cross-border compliance issues - the costs often far exceed the initial convenience. This article will clearly explain the regulatory and security focuses that Asian companies must face when using AI.

Singapore: A Benchmark for AI Governance in Asia

In terms of AI governance, Singapore is a pioneer in Asia. It has proposed a practical and operational AI governance framework, which emphasizes transparency, interpretability, human supervision, and risk classification, rather than a 'one-size-fits-all' approach. This pragmatic approach allows companies to use AI responsibly and enables regulation to keep pace, making it a model for many Asian regions.

Personal Data Protection Law is the Bottom Line: PDPA and Taiwan's Personal Data Protection Law

Regardless of whether in Singapore (PDPA), Taiwan (Personal Data Protection Law), or other Asian regions, personal data protection is the bottom line that cannot be crossed when using AI. Before analyzing customer lists or personal data using AI services, it is essential to confirm: does the original consent for collecting personal data cover this type of use? Will the data be used for model training? These are the most common areas where issues arise in practice.

Cross-Border Data: The Most Critical Aspect to be Cautious About

This point is particularly important for Taiwanese companies. Sending data to AI cloud services often means sending data to another country, subject to a different set of laws. As we have repeatedly warned in previous articles: when using Chinese AI services, data will enter China and be subject to local laws and regulations. The same logic applies to all cross-border services - before using them, it is essential to understand where the data will be stored, who can access it, and which country's laws will apply.

Five Compliance Focuses for Companies Introducing AI

Here is a practical checklist for teams introducing AI:

1. Data Classification. Classify data into publicly available, internal, confidential, and personal data levels, and clearly regulate which levels of data are not allowed to be used in external AI services.

2. Confirm Training Purposes. Check the service terms: will your input be used for model training? Enterprise solutions usually allow this to be turned off, but free versions often do not.

3. Maintain Human Supervision. AI outputs (especially those involving legal, financial, or personnel decisions) must be reviewed by humans, with responsibility lying with humans, not AI.

4. Choose the Right Deployment Method. In high-sensitivity scenarios, prioritize local deployment of open-source models, keeping data in your own hands.

5. Keep a Record. Record the use and decision-making process of AI, allowing for traceability in case of incidents, which is also a common requirement of governance frameworks.

In a Nutshell

AI governance is not about restricting the use of AI, but about enabling its safe and long-term use. By following the principles of 'keeping data where it should be and decision-making under human supervision', most risks can be avoided. This article is a general summary and not a legal opinion; please consult a professional for specific compliance advice.