ISO 42001 AI Model Log Template
AI Model Registry & Governance Log
The AI Model Log is a structured registry used to track, document, and monitor AI systems and models across their lifecycle. It enables organizations to maintain transparency, ensure accountability, and support compliance with standards such as ISO/IEC 42001. By recording key information such as purpose, data sources, algorithm type, ethical considerations, and risk status, this log provides a centralized overview of all deployed or in-development AI models, helping teams make informed decisions and maintain responsible AI practices.
Understanding the Model Log
Model ID
A unique identifier assigned to each AI model for consistent reference and tracking.
Version / Model ID
Internal version number or code of the model.
Entry Date
The date when this model was added to the log (automatically set to current date).
Responsible Person
Person or team accountable for the model lifecycle and governance.
AI System / Model Name
The AI system or model name being registered.
Deployment Environment
Where the model is deployed (e.g., production, test, PoC).
Model Purpose / Use Case
Business or technical purpose of the model.
Performance Metrics (e.g., accuracy, F1, AUC)
Key metrics used to evaluate model performance.
Model Type (e.g., classification, regression, generative)
Type of model based on learning objective.
Ethical Considerations (e.g., fairness, bias)
Identified ethical issues or risk mitigations.
Algorithm / Architecture Used
Main algorithm or architecture applied in the model.
Risk Assessment Status
Status of the AI risk log entry or assessment.
Training Dataset(s)
Name or description of datasets used for training.
Linked Policies / Documentation
References to supporting documents or policies.
Data Source(s)
Where the training data originated from.
Last Updated
Date when the model log entry was last updated.
Training Date
When the model was last trained or retrained.