Catalog
concept#Governance#Artificial Intelligence#Data#Security

Model Governance

Framework for controlling, monitoring and accountability of models, especially ML models. Focuses on compliance, reproducibility and lifecycle control.

Model governance defines processes, roles and rules for the safe, transparent and accountable use of models, particularly machine learning models.
Emerging
High

Classification

  • High
  • Organizational
  • Organizational
  • Intermediate

Technical context

Model registries (e.g., MLflow)Data catalogs and lineage toolsCI/CD and monitoring pipelines

Principles & goals

Clear responsibilities for model ownership and decisions.Traceability of all model versions and decisions.Automated monitoring combined with human oversight.
Run
Enterprise, Domain

Use cases & scenarios

Compromises

  • Overemphasis on control may stifle innovation.
  • Incomplete documentation leads to audit risks.
  • Poor data quality undermines governance measures.
  • Risk-based approach: stricter controls for high-risk models.
  • Automatic metadata capture at every deployment.
  • Schedule regular retrain and validation cycles.

I/O & resources

  • Training data, feature definitions, data diagnostics
  • Model artifacts, versions, hyperparameters
  • Risk classification, regulatory rules, responsibilities
  • Governance policy, review reports, audit trails
  • Registered model versions with metadata
  • Monitoring alerts, remediation plans, compliance statements

Description

Model governance defines processes, roles and rules for the safe, transparent and accountable use of models, particularly machine learning models. It aims at compliance, reproducibility and continuous monitoring across the model lifecycle. Implementation requires clear policies, assigned responsibilities and technical tool support.

✔

  • Improves compliance and reduces legal risks.
  • Increases trust through transparency and documentation.
  • Enables faster reaction to performance degradation.

✖

  • Implementation requires organizational effort and cultural change.
  • Not all models are equally auditable (black‑box models).
  • Initial tool integration and data pipelines are cost intensive.

  • Drift rate

    Share of models with significant data or performance drift per period.

  • Time-to-remediation

    Average time between detection of an incident and redeployment of a corrected model.

  • Documentation coverage

    Percentage of production models with complete validation and compliance documentation.

Bank: credit decision model

Establishing review processes, monitoring and documentation to comply with supervisory requirements.

E‑commerce: personalization models

Versioning, A/B test tracking and privacy reviews across the model lifecycle.

Insurance: claim classification

Documentation of training data, explainability reports and escalations on drift.

1

Inventory all models and classify by risk.

2

Define policies, roles and approval processes.

3

Introduce a central model registry and versioning.

4

Integrate automated monitoring and alerting for drift.

5

Conduct regular reviews, audits and trainings.

⚠️ Technical debt & bottlenecks

  • Missing metadata and incomplete model registry.
  • Fragmented toolchain without consistent integrations.
  • Hardcoded monitoring rules without parameterized control.
Data quality and lineageCross-functional alignmentTooling and integration fragmentation
  • Applying strict rules to all models regardless of risk.
  • Capturing documentation only on demand instead of continuously.
  • Automatically decommissioning models without remediation process.
  • Unclear ownership causes delays in escalations.
  • Undefined metrics make monitoring thresholds difficult.
  • Overengineering governance for low‑risk models.
Machine learning engineeringData and model validationGovernance, legal and compliance
Compliance requirements and auditabilityReproducibility and versioningScalable monitoring and alerting
  • • Regulatory requirements and reporting obligations
  • • Limited resources for monitoring and validation
  • • Heterogeneous tool landscape in the ML stack