NIST AI RMF vs MAS AI Guidelines: what they share and how they differ

NIST AI RMF and MAS AI Guidelines share 13 of 24 control themes and 16 checks. 73% of MAS AI Guidelines requirements can be proven with checks NIST AI RMF already uses, 39% the other way round.

USVoluntary frameworkVoluntary

NIST AI RMF

How to apply the NIST AI RMF to your AI systems

72 requirementsNext Dec 2026
SGSupervisory guidelinesUnder discussion

MAS AI Guidelines

Preparing for the MAS AI risk management guidelines

11 requirementsNext Dec 2026
13/24shared control themes
16shared checks
73%of MAS AI Guidelines requirements covered by NIST AI RMF evidence
39%of NIST AI RMF requirements covered by MAS AI Guidelines evidence

At a glance

NIST AI RMF MAS AI Guidelines
JurisdictionUnited States (federal)Singapore
KindVoluntary frameworkSupervisory guidelines
StatusVoluntaryUnder discussion
BindingNo sameNo same
Object analysedAI system sameAI system same
ScopeAny organisation designing, deploying or using AI.All financial institutions supervised by MAS (banks, insurers, asset managers, intermediaries).
Territorial reachNo territorial scope; de facto reference for US public buyers.Financial activities in Singapore, including branches of foreign groups.
PenaltiesNone (voluntary). Leverage: safe harbour in state laws.No penalty of their own. Once issued, they are supervisory expectations checked in inspections; shortcomings can lead to supervisory action.
Qualification axesInternal risk tier (organisation-defined)Use-case risk materiality (impact, complexity, reliance)
RolesAI actorFinancial institution
Requirements7211
Next milestoneDec 2026, Expected RMF revisionDec 2026, Expected issuance of the final guidelines

Theme by theme

requirements per theme

NIST AI RMFMAS AI Guidelines
Governance
13Governance & accountabilityshared1
2Inventory & categorisationshared1
2Literacy & competenceshared1
11Risk managementshared2
Assessment
6Impact assessmentsNIST AI RMF only
1Fairness & non-discriminationshared1
2Personal dataNIST AI RMF only
Build
Data governanceMAS AI Guidelines only1
8Technical documentationshared1
Logging & traceabilityMAS AI Guidelines only1
11Accuracy & robustnessshared1
1Cybersecurityshared1
1Environmental impactNIST AI RMF only
People & use
1Transparency & noticeshared1
3Human oversightshared1
4Explanation & redressNIST AI RMF only
Lifecycle & third parties
8Post-deployment monitoringshared1
6Incidents & corrective actionshared1
5Third parties & value chainshared1

What they share: one piece of evidence, two frameworks

16

CodeCheckRequirements NIST AI RMFRequirements MAS AI Guidelines
CHK-POL-RISKAn AI risk-management policy and process are established through transparent, documented controls
GOVERN-1.3GOVERN-1.4
MAS-4
CHK-RISK-TOLERANCERisk tolerances are defined and AI systems are assigned to risk levels
GOVERN-1.3GOVERN-5.2MAP-1.5MANAGE-1.2MANAGE-1.3
MAS-5.2
CHK-ROLES-CLARIFIEDRoles, responsibilities and delegated authorities are documented and clear to relevant stakeholders
GOVERN-1.4GOVERN-2.1GOVERN-2.3GOVERN-3.2GOVERN-5.1MANAGE-1.4
MAS-4
CHK-INVENTORYA mechanism to inventory AI systems is in place and resourced
GOVERN-1.6
MAS-5.1
CHK-TRAININGPersonnel and partners receive AI risk-management training
GOVERN-2.2
MAS-7
CHK-EXEC-ACCOUNTExecutive leadership is accountable for AI risk decisions (board committee, risk appetite)
GOVERN-2.3
MAS-4
VER-008-03Competent overseers assigned to the system
GOVERN-3.2MAP-3.5
MAS-6.2
CHK-THIRDPARTY-POLPolicies address third-party AI/data risks, incl. IP, transparency and testing
GOVERN-6.1MAP-4.1
MAS-6.7
CHK-CATEGORIZATIONAI system tasks and methods are categorized (classifier, generative, recommender)
MAP-2.1
MAS-5.1
CHK-TEVVTEVV plan, test sets, metrics and data considerations are documented
MAP-2.3MEASURE-2.1MEASURE-2.13
MAS-6.3
CHK-COMPETENCEOperator/practitioner proficiency processes and relevant standards are defined
MAP-3.4
MAS-7
CHK-INDEP-ASSESSIndependent or internal-expert assessment involves domain experts and affected communities
MEASURE-1.3MEASURE-4.2
MAS-6.3
CHK-BIASFairness and bias are evaluated and results documented
MEASURE-2.11
MAS-6.1
VER-003-01Documented and up-to-date risk register
MEASURE-3.1MEASURE-3.2
MAS-5.2
VER-008-02System designed to allow human oversight (stop button, override)
MANAGE-2.4
MAS-6.2
CHK-MODEL-MONITORINGPre-trained models used in development are monitored and maintained
MANAGE-3.2
MAS-6.7

Differences: requirements specific to each framework

Requirements with no check serving the other framework: the extra work.

NIST AI RMF

44

GOVERN-1.1
GOVERN-1.2
GOVERN-1.5
GOVERN-1.7
GOVERN-3.1
GOVERN-4.1
GOVERN-4.2
GOVERN-4.3
GOVERN-6.2
MAP-1.1
MAP-1.2
MAP-1.3
MAP-1.4
MAP-1.6
MAP-2.2
MAP-3.1
MAP-3.2
MAP-3.3
MAP-4.2
MAP-5.1
MAP-5.2
MEASURE-1.1
MEASURE-1.2
MEASURE-2.2
MEASURE-2.3
MEASURE-2.4
MEASURE-2.5
MEASURE-2.6
MEASURE-2.7
MEASURE-2.8
MEASURE-2.9
MEASURE-2.10
MEASURE-2.12
MEASURE-3.3
MEASURE-4.1
MEASURE-4.3
MANAGE-1.1
MANAGE-2.1
MANAGE-2.2
MANAGE-2.3
MANAGE-3.1
MANAGE-4.1
MANAGE-4.2
MANAGE-4.3

MAS AI Guidelines

3

MAS-6.4
MAS-6.5
MAS-6.6

Timelines

PastSet in the textPotentialTo verify
Nov 12, 2018MAS AI Guidelines · FEAT principles (fairness, ethics, accountability, transparency)
Jan 26, 2023NIST AI RMF · AI RMF 1.0
Jul 26, 2024NIST AI RMF · Generative AI Profile (NIST AI 600-1)
Dec 2024MAS AI Guidelines · Information paper on AI model risk management
Jul 23, 2025NIST AI RMF · AI Action Plan asks for a revision of the framework
Nov 13, 2025MAS AI Guidelines · Public consultation on the guidelines
Jan 31, 2026MAS AI Guidelines · Consultation closes
Dec 2026NIST AI RMF · Expected RMF revision
Dec 2026MAS AI Guidelines · Expected issuance of the final guidelines
Dec 2027MAS AI Guidelines · End of the 12-month transition (if issued late 2026)

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