Sound Practices for Responsible Adoption of Artificial Intelligence (Consultation Report)

Applies To

Overview

Published in June 2026 as a consultation report, this FSB document offers a structured menu of 12 sound practices organized around AI governance and operational risk management in financial services. The practices span the entire AI lifecycle, from initial design and data governance through deployment, monitoring, and decommissioning. Key provisions address model risk management frameworks, accountability structures, third-party and vendor oversight, and the need for ongoing human review of AI-driven decisions. The report is consultative and non-binding in its current form. However, FSB outputs carry significant weight with national supervisors and central banks, and the practices are expected to inform domestic regulatory guidance across member jurisdictions. The consultation closed on 22 July 2026, the FSB published the responses on 6 August 2026, and it expects to publish a final report in the coming months. The FSB presents the practices as a non-prescriptive menu that any financial institution could adopt, applied in proportion to its use of AI.

Key Requirements

• •The practices are a non-prescriptive menu; the FSB says they are not an international standard.
• •They are addressed to financial institutions of all types, applied in proportion to their AI use.
• •Governance: clear accountability, oversight, and escalation for AI systems.
• •Model risk: validation and independent review scaled to how material each model is.
• •Third parties: due diligence and contract terms for AI sourced from vendors.
• •Monitoring and human review: ongoing performance checks, and human review at high-impact decision points.

What Your Organization Must Do

• →Read the 12 sound practices as a voluntary menu, not as binding rules.
• →Map each practice to existing governance, model risk, vendor oversight, and lifecycle controls.
• →Scale validation and independent review of AI models to how material each model is.
• →Review AI vendor due diligence and contracts for transparency and audit rights.
• →Document where human review sits in high-impact AI decisions and name accountable owners.
• →Track the FSB final report and any domestic supervisory guidance that follows it.

Playbook Guidance

Step-by-step implementation guidance for compliance teams.

Frequently Asked Questions

Is the FSB AI Sound Practices report legally binding on banks and insurers?

No, the June 2026 consultation report is non-binding in its current form. However, FSB outputs carry substantial influence with national supervisors and central banks across member jurisdictions, so financial institutions should expect these 12 practices to shape domestic regulatory expectations in the near term.

Which financial institutions are in scope for the FSB AI Sound Practices?

The practices are addressed to all types of financial institutions, not only banks and insurers. The FSB expects firms to apply them in proportion to how they use AI.

How do the FSB AI Sound Practices align with existing model risk management frameworks like SR 11-7?

The FSB practices extend traditional model risk management principles to cover machine learning and generative AI, requiring validation and independent review scaled to model materiality. Institutions already complying with SR 11-7 or equivalent guidance will need to update their frameworks to address AI-specific lifecycle controls, drift detection, and outcome fairness assessments.

What third-party vendor oversight does the FSB AI consultation report require?

It requires nothing, because the practices are a non-prescriptive menu. Among the options are due diligence on AI vendors and contract terms such as transparency and audit rights.

What is the deadline to submit a response to the FSB AI Sound Practices consultation?

The consultation closed on 22 July 2026, and the FSB published the responses on 6 August 2026. It expects to publish the final report in the coming months.

What human oversight requirements do the FSB AI Sound Practices impose for high-impact decisions?

The practices require embedded human review mechanisms at critical decision points, particularly where AI-driven determinations affect customers or financial stability. Institutions should document where these review checkpoints sit within their workflows and assign named accountable owners to each material AI system.