Manage data quality, model development and monitoring across the lifecycle

Practical action

Maintain data lineage records and a model monitoring plan.

Who does it apply to?

Voluntary for any organisation building or deploying AI.

Actors

Provider / developerDeployer / user organisation

Sectors

Cross-sector / all sectors

Use cases

AI in finance, credit and insuranceGenerative AI and foundation models

Which controls meet this duty?

Satisfies: the control, operated properly, does the work the duty asks for. Supports: it contributes but the duty needs more. Each control page lists every other duty it serves, so work done once can be counted once.

• satisfiesProcessData governance lead · once per ai system
  Data governance and dataset documentation

  Serves 8 recorded duties · evidence: Dataset documentation sheet, Data quality and bias check report, Dataset approval for use

  Data lineage and dataset bias minimisation.

• satisfiesTechnical measureAI system owner · continuous
  Post-deployment monitoring and drift detection

  Serves 10 recorded duties · evidence: Post-market monitoring plan, Monitoring dashboard or periodic monitoring report, Monitoring review decision

  Active monitoring and regular tuning after deployment.

• supportsTechnical measureQuality or testing lead · at launch and on material change
  Accuracy, robustness, fairness and security testing

  Serves 15 recorded duties · evidence: Pre-release test report, Test plan and acceptance criteria, Release test sign-off

  Repeatability and robustness evidence before release.

Templates that cover this duty

Generated from the records. Free · no account · links emailed to your work address. This duty is cited in each file with its source reference and a link back here.

• ISO/IEC 42001 Gap Assessment and Statement of Applicability

  Assessment · XLSX · v3

• NIST AI RMF ↔ EU AI Act ↔ ISO/IEC 42001 Crosswalk

  Crosswalk · XLSX · v5

• AI Data Governance Register

  Register · XLSX and DOCX · v3

• AI Audit Evidence Tracker

  Register · XLSX · v1

What evidence would a reviewer expect?

Framework mappings

Original editorial crosswalks. They cite clause numbers only and reproduce no standard text; confidence reflects how direct the mapping is.

See every Singapore duty mapped this way →

Frequently asked questions

Cite this record

AIPolicyTracker (2026). “Manage data quality, model development and monitoring across the lifecycle (Singapore Model AI Governance Framework)” [AIPT-OBL-0056, verified 26 September 2026 by Bhaskar Bhatt]. https://aipolicytracker.org/id/AIPT-OBL-0056 (accessed 8 November 2026). Data licensed CC BY 4.0.

Permanent id AIPT-OBL-0056: it never changes or gets reused, and its address keeps working if this page moves.

Cite the official text alongside it: Model AI Governance Framework (Second Edition), Personal Data Protection Commission Singapore and IMDA, https://www.pdpc.gov.sg/help-and-resources/2020/01/model-ai-governance-framework.

@misc{aipolicytracker2026_managedataqualitymodelde,
  author       = {{AIPolicyTracker}},
  title        = {{Manage data quality, model development and monitoring across the lifecycle (Singapore Model AI Governance Framework)}},
  year         = {2026},
  howpublished = {\url{https://aipolicytracker.org/obligations/singapore-mgf-operations-management}},
  url          = {https://aipolicytracker.org/obligations/singapore-mgf-operations-management},
  urldate      = {2026-11-08},
  note         = {Data licensed CC BY 4.0. Accessed 8 November 2026}
}

Change history

Similar obligations in other instruments

• Apply data governance and quality criteria to training, validation and testing data — EU AI Act, European Union
• Deployers must ensure input data they control is relevant and representative — EU AI Act, European Union