FSB finalises sound practices for the responsible adoption of artificial intelligence (AI)

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Ref: 14/2026

• The 12 sound practices emphasise the importance of organisation-wide AI governance and highlight considerations for managing risks throughout the AI lifecycle.
• The sound practices aim to promote coordination, cooperation and information-sharing among financial institutions, supervisors and other stakeholders, within and across jurisdictions, as AI adoption increases.
• The FSB strongly encourages the board and senior management of financial institutions to reference the sound practices when developing and refining their institution’s AI governance, risk management and control frameworks.

The Financial Stability Board (FSB) today published its final report on Sound Practices for Responsible Adoption of Artificial Intelligence, reflecting public feedback received on a consultative version of the report, published in June 2026.

The report identifies 12 sound practices to help financial institutions harness the benefits of AI while managing its risks responsibly, in ways that support the resilience of the financial system. The 12 sound practices are organised into two groups, which:

1. Emphasise the importance of organisation-wide AI governance as financial institutions adopt AI and highlight: (i) the board and senior management’s pivotal role in setting and overseeing alignment between a financial institution’s AI adoption and its business model, risk appetite, and strategy; (ii) maintaining an appropriate governance framework with well-defined roles and responsibilities; (iii) incorporating AI risks into the risk management framework and effective documentation of AI use; and (iv) undertaking continuous learning and adaptation.
2. Set out a comprehensive framework for managing risks throughout the AI lifecycle from AI development and deployment, focussing on: (v) effective and systematic assessment of the materiality and risks associated with AI use cases at inception and thereafter; (vi) selection of appropriate AI technologies; (vii) appropriate data governance; (viii) explainability of various types of AI and consideration of compensating controls; (ix) evaluation of the performance of AI use cases; (x) appropriate and effective human oversight; (xi) management of AI-related cyber and information and communication technology (ICT) risks; and (xii) management of risks from third-party AI dependencies.