What MAS expects, and
when it expects it.
From a clean read of the November 2025 consultation paper P017-2025 and the proposed Guidelines on AI Risk Management. Five expectation areas, ten consultation questions, one transition period, one applicability gate. Below is the picture without the commentary noise.
§ 01 — The five expectation areasWhat MAS asks FIs to do.
The Guidelines are organised around five sections (numbered as MAS numbers them in Figure 1 of the paper). Each section sets supervisory expectations. None creates new prohibitions; all are calibrated by proportionality. Read down this list and the ask is unambiguous.
- Board approves governance approach, sets risk appetite, ensures sufficient AI literacy to oversee and challenge.
- Senior management implements, escalates, allocates resources, regularly reviews effectiveness.
- AI Identification — clear definitions, criteria, processes (with robust systems) to consistently identify AI usage across all business and functional areas. Designated control function as final arbiter.
- AI Inventory — accurate, up-to-date inventory of AI use cases, systems and models. Either dedicated, or enhancement of existing inventories with clear linkages. Captures purpose, scope, model type, data, dependencies, lifecycle status, risk-materiality rating, validation status, owners.
- Risk Materiality Assessment — assessment methodology evaluating impact, complexity, reliance. Inherent and residual risk both assessed. Residual must meet risk appetite before deployment. Reviewed regularly.
- Data management — fit-for-purpose, representative, quality, classification, security, privacy, lineage.
- Transparency & explainability — calibrated to risk; higher standards for credit, underwriting, advisory, fund management.
- Fairness — define "fair" outcomes, identify and mitigate harmful bias across the lifecycle.
- Human oversight — roles, capabilities, design, documentation; counter automation bias and decision fatigue.
- Third-party AI — onboarding, transparency, supply-chain, concentration, contingency, legal, capability, complexity.
- Selection — justify and document algorithm and feature choices; weigh complexity against risk.
- Evaluation & testing — measures, methods (incl. adversarial & stress), overfitting mitigation, key failure modes.
- Technology & cybersecurity — security, access control, third-party components.
- Reproducibility & auditability — full documentation across data, training, evaluation, explainability, fairness.
- Pre-deployment reviews — independent for high-risk; peer reviews otherwise; technology & cyber sign-off.
- Post-deployment monitoring & change management — drift, incidents, kill switches, retirement protocols.
- AI risk-management capabilities — competent personnel; recruit talent; train staff; allocate human / technological / financial resources proportionate to AI risk profile. Regular review of training programmes including coverage of newer AI technologies.
- Technology infrastructure — adequate hardware (e.g. GPU), network, memory, secure data pipelines for performance, scalability, resilience. Aligned to MAS Technology Risk Management Guidelines.
- Step 1 — does the FI have AI use cases, systems, or models? If no → basic policies only (Annex §5).
- Step 2 — is AI used as an integrated part of business processes? If no → basic policies only. If yes → §2 oversight + §3 risk management systems apply in full; §4 lifecycle controls and §5 capabilities apply proportionate to risk materiality of each use case.
§ 02 — Proportionality, as MAS draws itThree tiers, one decision flow.
Figure 2 of the consultation paper draws this as a decision tree. Below is the same logic with each tier's binding obligations spelled out. Where you land determines what applies.
Decision flow · which obligations bind
From Annex §1–§4Basic policies only.
- Senior-management AI owner designated.
- Allowed/disallowed uses defined.
- Approved-tools list maintained.
- Communication, checks, annual review.
- Human review of AI outputs before use.
Basic policies only.
- Examples (Annex §3): individual LLM use to draft emails; AI grammar/spell tools; AI to summarise research; AI image generation in marketing; ad-hoc claims-document review.
- Humans use AI assistively and check outputs.
- Same five basic-policy elements as Tier 1.
Full Guidelines apply.
- §2 Oversight — board, senior management, framework, policies, risk appetite — in full.
- §3 Risk-management systems — identification, inventory, materiality assessment — in full.
- §4 Lifecycle controls — 11 domains — proportionate to risk materiality of each use case.
- §5 Capability & capacity — proportionate to AI exposure.
- Annex §4 examples: legal contract review, IT helpdesk chatbot, financial data extraction, claims, advisory.
§ 03 — The lifecycle, as MAS describes itEleven control domains, one lifecycle.
Section 4 of the Guidelines is the most operationally detailed. It specifies eleven control domains arranged across the AI lifecycle. Below is the canonical sequence — from intake through retirement — with the binding control at each gate.
Plus the cross-cutting layer.
Three controls run across all eleven gates rather than at any one of them: third-party AI management (transparency, supply-chain, concentration, contingency, legal, capability), human oversight (roles, capabilities, design, documentation), and aggregate-portfolio review (periodic re-validation of high-risk AI; aggregate risk view across all deployed AI). These are present at every gate, owned at every gate, and evidenced at every gate.
§ 04 — Materiality, in three dimensionsImpact · Complexity · Reliance.
MAS specifies the minimum dimensions in §3.10 of the Guidelines. Each AI use case is assessed against all three, both inherent (before controls) and residual (after controls). Residual must meet the FI's risk appetite before deployment (§3.9). Below is the working visualisation an architect would put into the inventory.
The third dimension — Reliance (autonomy of the AI, level of human oversight, availability of alternatives) — is captured for each use case as a separate axis on the inventory record. Higher reliance shifts the use case toward H/H+ regardless of where it sits on impact × complexity.
§ 05 — The timelineFrom consultation to compliance.
MAS proposes a 12-month transition after issuance (§4.7 of the consultation paper, also Question 10). Issue date is not yet announced; the consultation closed 31 January 2026. Below is the indicative working calendar an architect should plan against — assuming Q3 2026 issuance, which is consistent with MAS's typical post-consultation cadence.
The architect's working dates.
- › 13 Nov 2025 — Consultation paper P017-2025 published.
- › 31 Jan 2026 — Consultation closes. 10 questions to answer.
- › By Jan 2026 — MindForge AI Risk Management Handbook released as companion guide (per §2.5 footnote 5).
- › T₀ (issue date, not announced) — Final Guidelines issued. Most likely Q3 2026 if MAS follows typical post-consultation cadence; could be later.
- › T₀ + 12 months — End of transition. FIs are expected to have assessed and implemented the Guidelines as appropriate.
Honest caveat: the issue date for the final Guidelines is not stated in P017-2025. Q3 2026 is an inference, not a published date. If the consultation produces material changes, issuance could slip into Q4 2026 or 2027. Architects should plan to a moving T₀ but a fixed 12-month window from whatever T₀ turns out to be.
§ 06 — The ten questions on the tableWhat MAS is actually asking.
The Guidelines are at consultation stage, which means the structure described above is provisional. MAS asks ten questions in §5 of the paper. Reading the questions tells you where MAS itself sees the open ground.
Three of these questions sit closer to the architectural surface than the others. Q4 is asking how to operationalise materiality at the organisational level — i.e. when does aggregate AI exposure trip the cross-functional-committee requirement. Q7 is asking whether impact/complexity/reliance is the right minimum set. Q10 is asking whether 12 months is enough — and for institutions with mature MRM, it usually is; for those starting from a CMDB and a slide deck, it usually isn't.
Read across all five expectation areas and the message is consistent. MAS expects identifiable, inventoried, materiality-assessed AI; controls calibrated to materiality, not status; and capabilities and infrastructure that are real, not declared. The 12-month transition is generous against a clean starting point and tight against a messy one. The proportionality flow makes the obligations smaller for FIs with light AI use; it does not make them softer for FIs with integrated use.
For the architect, the unit of work is the inventory record. Every AI use case in the FI either has one (with materiality, lifecycle status, owner, validation, third-party links, evidence trail) or it does not. By T+12, every use case must have one. That is the test the supervisor will run.