AI Vendor Assessment for SG

SG-specific obligations covered

The output is anchored on the regulations that apply to AI deployments in SG. The top frameworks cited:

• Personal Data Protection Act 2012 (PDPA, as amended 2020)legislation · In force

  Organisations must obtain consent for personal data collection, implement data protection policies commensurate with data sensitivity, notify the PDPC and affected individuals of significant data breaches, and appoint a Data Protection Officer.

• Model AI Governance Framework for Generative AI (IMDA/PDPC, 2024)voluntary_framework · Voluntary

  Organisations should establish internal AI governance structures, conduct regular risk assessments of AI systems for safety and fairness, implement human oversight mechanisms proportionate to decision consequence, and communicate AI use transparently to affected stakeholders.

• MAS Principles on Fairness, Ethics, Accountability and Transparency (FEAT)regulatory_principles · In force

  Financial institutions must ensure AI and data analytics deployed in financial services are fair, ethical, accountable, and transparent, and should conduct FEAT self-assessments using the Veritas Fairness Assessment Methodology.

• MAS Advisory on Use of Generative AI in Financial Services (2024)regulatory_guidance · In force

  Financial institutions deploying generative AI must implement robust governance frameworks, maintain human oversight for material financial decisions, ensure data quality and provenance, conduct ongoing model performance monitoring, and manage third-party AI vendor risks.

How the AI Vendor Assessment approaches this

You describe the vendor (name and product or service) and your organisation's context — jurisdiction, industry, staff size, risk appetite — and choose your alignment framework: ISO/IEC 42001:2023, NIST AI RMF, or both. The tool produces a structured, evidence-based assessment ready to hand to your procurement, legal, and information-security teams.

The Executive Summary Word document is a one-page sign-off artifact — recommendation (Approved / Conditional / Rejected), top three risk flags, top three strengths, sign-off block. The detailed Excel workbook is the working assessment instrument: 30 questions across six weighted categories, with evidence guidance, regulatory call-outs, and an auto-summing scoring sheet. Both are AI-assisted drafting aids intended to accelerate review by qualified practitioners.

What you get

• ✓ Four deliverables, three jobs: Executive Summary (.docx) for board sign-off, Detailed Workbook (.xlsx) for the working scoring (with the Evidence Request List on a dedicated tab inside it), Procurement Checklist (.xlsx) for foundational readiness — no overlap, no confusion.
• ✓ Aligned to ISO/IEC 42001:2023 Annex A or NIST AI Risk Management Framework — your choice. Every question carries the framework reference and (where applicable) jurisdiction-critical regulatory call-outs.
• ✓ Excel formulas auto-sum each category total, calculate the weighted overall percentage, and surface Pass / Conditional / Reject thresholds — procurement teams don't have to re-key or re-calculate.
• ✓ Tailored to the vendor's product category, your industry, jurisdiction, and organisation size — not a generic checklist. Designed for review and sign-off by qualified procurement, legal, or information-security practitioners.

Ready to generate?

$29 · one-time — answer a 6-question intake (including jurisdiction = SG), and download your tailored document immediately.

Also available framed for your sector → see industry-specific pages