When does the Singapore AI Governance Framework take effect?

The Singapore Model AI Governance Framework took effect on January 21, 2020.

What are the key requirements of the Singapore AI Governance Framework?

Internal governance: clear roles and responsibilities for AI oversight, risk management policies, staff training Decision type mapping: choose the level of human involvement to match the risk of the decision Operations: data quality, model testing, bias monitoring, and incident response

International·In Effect·Effective January 21, 2020

Singapore Model AI Governance Framework

Dates and status re-checked against primary sources on September 30, 2026. How we verify

Published by Singapore's Personal Data Protection Commission (PDPC), the second edition of the Model AI Governance Framework (January 21, 2020) provides non-binding, practical guidance for organizations deploying AI. It is organized around four areas: internal governance structures and measures, the level of human involvement in AI-augmented decisions, operations management, and stakeholder interaction and communication. IMDA added a Model AI Governance Framework for Generative AI on May 30, 2024. The Monetary Authority of Singapore has separate guidance for financial institutions, including its 2018 FEAT principles.

What this means for your team

The Singapore framework is one of the clearest practical guides available for building an AI governance program, even if you do not operate in Singapore. Its companion Compendium of Use Cases gives real-world examples. If you serve Singapore financial institutions, expect their own requirements: MAS issued the FEAT principles (Fairness, Ethics, Accountability, Transparency) in 2018 and proposed AI risk management guidelines for the sector in November 2025. FEAT is a useful governance summary well beyond Singapore.

Key requirements

Internal governance: clear roles and responsibilities for AI oversight, risk management policies, staff training
Decision type mapping: choose the level of human involvement to match the risk of the decision
Operations: data quality, model testing, bias monitoring, and incident response
Stakeholder interaction: disclose AI use to affected individuals, provide recourse mechanisms
Related, separate guidance: MAS FEAT principles (2018) for financial institutions

Singapore Model AI Governance Framework

What this means for your team

Key requirements

Further reading