AI security & compliance
Building a Model Inventory for Risk Management
A gated worksheet template to help enterprises develop a structured model inventory, supporting effective risk management and compliance in AI deployments.
An up-to-date, comprehensive model inventory is essential for managing AI risk and meeting compliance requirements. This worksheet guides platform engineering leads, risk managers, and AI practitioners through the process of cataloging AI models across the enterprise.
The interactive format captures details about model purpose, ownership, lifecycle stage, input data, performance metrics, and risk classification. These elements align with governance frameworks from the likes of the Office of the Comptroller of the Currency and industry best practices.
Inputs
Enter the internal name or identifier of the model.
Person or team responsible for the model's development and maintenance.
Brief description of the business function or decision supported.
Select the current lifecycle stage of the model.
List the data sets or streams used for training and inference.
Specify metrics tracked such as accuracy, precision, recall, AUC.
Classify the model's risk level according to your governance framework.
How often the model undergoes formal review or validation.
Result
if (riskClassification == 'high') { return 3 } else if (riskClassification == 'moderate') { return 2 } else { return 1 }Risk level summary
The model is classified as low-to-moderate risk. Establish regular monitoring and validation schedules consistent with this level.
Best practice
A centralized model inventory should be integrated with overall AI governance platforms and include version control and audit metadata to support regulatory compliance and internal risk tracking.
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