Playbook · Singapore · Financial services

AI governance for Singapore banks & financial services:
a supervisor-ready roadmap

The MAS FEAT principles, Veritas, Project MindForge and the new MAS AI Risk Management Guidelines — plus the PDPA — now define how Singapore financial institutions build, deploy and prove their AI. Enterprise buyers ask for the evidence in every security review. Here is the 5-phase roadmap MAS and buyers inspect, and the runtime gap your existing stack cannot close.

Book a 20-min fit call Get the 2-page roadmap (PDF)
MAS FEAT Veritas · MindForge MAS AI Risk Mgmt Guidelines PDPA · OWASP & ATLAS
The mandate

What MAS and buyers now expect

MAS sets supervisory expectations, not a single AI statute. Three bodies of guidance set the bar for any Singapore financial firm building or deploying AI.

FEAT + AI Risk Mgmt Guidelines

MAS · supervisory

Fairness, Ethics, Accountability, Transparency (2018), now reinforced by the MAS AI Risk Management Guidelines (consultation Nov 2025): board oversight, AI risk systems and lifecycle controls.

Veritas & Project MindForge

Toolkits · industry-built

Veritas operationalises FEAT with fairness assessment methodology and metrics; MindForge extends it to a generative-AI risk framework built with the banks.

PDPA + MAS TRM

Enforceable · data & tech risk

The Personal Data Protection Act for data, plus MAS Technology Risk Management and Outsourcing guidelines that apply to AI systems and third parties.

Overlays that may apply: ISO/IEC 42001 and the NIST AI RMF as control baselines regional and global buyers reference · EU AI Act if you serve the EU · OWASP LLM Top 10 & MITRE ATLAS for AI security.
The through-line

What every MAS expectation converges on

Accountability sits with the institution, not the model
Fairness & ethics tested across the lifecycle
Human oversight & transparency of AI decisions
Documented evidence, not assertions
The roadmap

Five phases MAS & buyers will inspect

A supervisor-ready sequence that maps to FEAT, the MAS AI Risk Management Guidelines, MAS TRM and the PDPA.

PHASE 0
Govern
Own the risk before AI ships
AI policy & board oversight · AI inventory & risk tiering · FEAT accountability · roles & sign-off
PHASE 1
Build & Test
Prove it's safe & fair before it ships
Red-teaming & fairness testing · OWASP LLM Top 10 & MITRE ATLAS · Veritas-style assessment · MindForge GenAI risks
PHASE 2
Deploy & Enforce
Control what the AI can access & do
Access control & agent guardrails · human oversight · shadow-AI & PDPA data controls · MAS TRM
PHASE 3
Monitor & Respond
See it, contain it, report it
Monitoring & logging · drift & output checks · incident response · outsourcing & third-party oversight
CONTINUOUS
Assure & Disclose
Answer MAS, buyers & the board
Audit-ready evidence · ISO 42001 alignment · transparency disclosures · security-questionnaire responses
The readiness gap

Why this is urgent, not theoretical

Adoption has outpaced control — and that gap is the exposure.

88%
had a confirmed or suspected AI-agent security incident this year
80%
report moderate-to-pervasive shadow AI
18%
have a formal AI security policy
21%
have mature governance for AI agents
Sources: Gravitee (survey of 919 organizations), ISACA & industry surveys, 2026.
The blind spot

The gap your existing stack cannot close

Your IAM/PAM, WAF and API gateways, DLP/CASB, SIEM and EDR are built for deterministic software. None can catch a legitimately-authorised AI agent, driven by untrusted input, doing something inside its permissions but outside your policy — the dominant 2026 failure mode (prompt injection, the "lethal trifecta", agentic data exfiltration).
IAM / PAM
identity authenticated, in scope
waved through
WAF / API gateway
request well-formed
waved through
DLP / CASB
sanctioned tool & channel
waved through
SIEM / EDR
no known-bad signature
waved through
Every layer says "yes." None can see the AI's intent vs your policy.
Where Trampolyne fits

Make your AI stay within intended bounds — and prove it

From pre-deployment testing, through runtime, to audit-ready evidence. Trampolyne supplies the technical controls and evidence that make each phase credible — the policies, documentation and processes stay yours.

Product Govern Build & Test Deploy & Enforce Monitor & Respond Assure
AI Red-Teaming
Shadow AI Detection & Runtime Control
Enterprise AI Security & Runtime Control
AI Compliance Assistant
● core control & evidence Trampolyne provides  ·  ◐ supporting input.

AI Red-TeamingAWS Marketplace

Continuous adversarial testing on a scheduled cadence and on every material change. 27+ attack classes, working exploits + fixes, mapped to OWASP LLM Top 10 & MITRE ATLAS — the validation and buyer evidence your security reviews ask for.

OWASP LLM Top 10MITRE ATLASMindForge GenAI risksFairness testing input

Enterprise AI Security & Runtime Control

Sits inline and evaluates every request before the model or agent acts — RBAC/ABAC/PBAC/NGAC enforcement in milliseconds. A deterministic guardrail over a non-deterministic model. No model rewrites.

Human oversightMAS TRMISO 42001 controls

Shadow AI Detection & Runtime Control

Sits between employees and every public AI tool. Classifies data by type, provenance and role, blocks sensitive data before it leaves, and logs every interaction — without killing productivity.

Data leakage to public AIPDPAISO 42001 data controls

AI Compliance Assistant

Turns enforcement, red-team and runtime evidence into review-ready answers — a continuous, per-decision audit trail for MAS supervision, your board and enterprise-buyer security questionnaires.

ISO 42001 audit evidenceFEAT transparency evidenceBoard reporting
FAQ

AI governance questions we hear from Singapore financial firms

How does MAS regulate AI in financial services?
MAS sets supervisory expectations rather than a single AI law. The foundations are the FEAT principles (2018), the Veritas fairness-assessment toolkit, and Project MindForge's generative-AI risk framework. In November 2025 MAS published a consultation on Guidelines on AI Risk Management for Financial Institutions, covering board and senior-management oversight, AI risk systems and lifecycle controls. The PDPA and MAS TRM and Outsourcing guidelines continue to apply.
What are the MAS FEAT principles?
FEAT stands for Fairness, Ethics, Accountability and Transparency — principles MAS co-created with the industry in 2018. Veritas operationalised FEAT with a fairness-assessment methodology, metrics libraries and templates, and Project MindForge extended the thinking to generative AI.
Do the PDPA and generative AI interact?
Yes. The PDPA governs how personal data may be collected, used and disclosed — including when it flows into or out of generative-AI systems. Institutions must ensure a valid basis, protect data in transit and at rest, and prevent outputs from leaking personal data. Runtime controls that classify and block sensitive data before it reaches a public model meet this expectation directly.
Can our existing security stack secure AI agents?
No. IAM, DLP, WAF and SIEM are built for deterministic software. They cannot catch a legitimately-authorised AI agent, driven by untrusted input, doing something inside its permissions but outside your policy — the dominant 2026 failure mode. MAS's technology-risk and AI lifecycle expectations point directly at closing this runtime gap.
More playbooks

Governance playbooks for other markets

Same 5-phase structure, tuned to each market’s regulators and buyers.

Global · All sectors United States · BFSI United Kingdom · BFSI UAE · BFSI Saudi Arabia · BFSI India · BFSI Gulf / GCC · All sectors

See where your Singapore AI program stands

Take the 2-page roadmap to your board, MAS relationship and buyers — then pressure-test your position in a 20-minute call.

See if we are a fit — 20-min call Download the 2-page roadmap (PDF)
Positioning collateral, not legal advice. Trampolyne AI helps you align with regulatory and supervisory expectations; it does not issue certifications.