AI Data Governance: From Can We Use It to Should We Use It

Purpose, legality, quality, sensitivity, retention, access and deletion are seven questions every AI project must answer.

Start by Clarifying the Operating Impact

Possessing data does not permit every use. AI projects must address collection purpose, personal and confidential classification, third-party terms, retention and deletion.

Design Principles

• Use data for explicit compatible purposes
• Minimize data to what the task needs
• Observe and track quality issues
• Map vendors, models and logs in the data flow

Practical Implementation Steps

1. Inventory fields and sources
2. Classify personal, confidential and contractual limits
3. Confirm processing basis and scope
4. Set access, retention, masking and deletion
5. Record destinations and processors

Keep baselines, decision rationale and results at every step so the next expansion is based on evidence rather than memory.

Decision Note

If the team cannot explain origin, destination, access and deletion, the data is not ready for an AI workflow.

Research and Policy Sources

This guide reorganizes the following official frameworks, policies and research into a practical adoption method.

• NIST AI Risk Management Framework
• Taiwan MODA: AI risk classification framework
• Taiwan Executive Yuan: AI Basic Act policy overview