On 3 July 2026, the Monetary Authority of Singapore (“MAS“), together with leading financial institutions (“FIs“) and financial technology companies, published the “Safeguards for Agentic Finance at Runtime” (“SAFR“) industry white paper on developing safeguards for artificial intelligence (“AI“) agents in finance.
As AI agents in financial services increasingly carry out tasks autonomously and at a speed beyond practical human intervention, FIs need real-time safeguards to ensure that such behaviour remains within the pre-defined mandates, policies and risk boundaries set by them. Thus, the paper proposes the SAFR framework that enables AI agents in financial services to carry out financial tasks safely, securely and reliably.
- Governance checkpoints: The SAFR framework provides a set of governance checkpoints between every agent decision and its execution, that verifies and records an AI agent’s proposed actions before the execution of its tasks. It consists of four runtime components interacting through the governance envelope as a single, structured record of what the agent intends to do, why it intends to do it, and what evidence supports the decision. Together, these ensure that no agentic action reaches execution without having been declared, authorised and assessed. The four runtime components are as follows:
- Agent identity: This component binds each proposed action to a recognised, registered agent, that is verified against that agent’s registry entry, before any other evaluation proceeds.
- Controls repository: This component is the institution’s configurable rulebook. It comprises the controls (drawn from sources such as organisational policies, regulatory requirements, product rules, and user-provided mandates) against which a proposed action is checked.
- Disposition engine: This component evaluates each in-scope action against the controls retrieved from the Controls Repository and resolves it to one of the possible outcomes (i.e. deny, escalate, auto-execute, or observe).
- Audit log: This component is a tamper-evident, append-only record of every governance decision.
- Operationalisation at point of action: The SAFR framework builds on MAS’ Project Mindforge AI Risk Management toolkit, with a focus on how safeguards can be operationalised at the point of action for AI agents. The paper sets out the direction for how these safeguards, including policy-bound execution, real-time validation, auditability and interoperability, can be embedded into system operations so that FIs can deploy AI agents with trust and consistency.
- Case studies: The SAFR framework has already been applied across various use cases, including:
- Agent-assisted payments and treasury operations: Where autonomous agents can execute routine transactions within predefined mandates, improving efficiency and reducing operational friction.
- Wealth management and advisory workflows: Where AI agents review documents and generate structured assessments within narrowly scoped task boundaries, supporting faster and more consistent compliance review.
- Client engagement: Where AI agents generate client insights and draft materials within approved content boundaries, enabling staff to engage clients more effectively and productively.
Click on the following link for more information:
- MAS Media Release titled “MAS Partners Industry to develop Safeguards for AI Agents in Finance” (available on the MAS website at www.mas.gov.sg)
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