Turning Generative AI Risk into Practical Controls

Translate NIST and Taiwan risk frameworks into controls a delivery team can implement across governance, context, measurement and response.

Start by Clarifying the Operating Impact

AI governance is neither a blanket ban nor a promise that models never fail. It starts by mapping context and harm, then applying controls proportionate to impact and likelihood.

Design Principles

• Assign accountable business ownership
• Assess risk by use case, not model name
• Require human authorization for high-impact actions
• Evaluate, log and improve through the lifecycle

Practical Implementation Steps

1. List users, affected parties and data types
2. Identify hallucination, privacy, bias, security and overreliance
3. Tier by impact and reversibility
4. Add access, citation, review, audit and fallback controls
5. Retest real cases and record incidents

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

Decision Note

Controls should match risk. Low-risk summaries may use sampling; payments, termination, medical or legal decisions should never be completed by a model alone.

Research and Policy Sources

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

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