AI model inventory

An AI model inventory is a centralized list of all AI models developed, deployed, or used within an organization. It captures key information such as the model’s purpose, owner, training data, risk level, and compliance status. 

The inventory helps organizations manage their AI assets more systematically.

Why it matters

An AI model inventory is critical for AI governance, compliance, and risk teams. It provides visibility into where and how AI is being used, enabling organizations to monitor model performance, ensure regulatory compliance, and quickly respond to audits. 

Without an accurate inventory, AI risks can go unnoticed, leading to legal, ethical, and operational challenges.

Real world example

A healthcare company uses AI models to predict patient readmission risks and assist with diagnostics. By maintaining an AI model inventory, the company ensures that only validated models are used in clinical settings and that every model complies with data privacy laws like HIPAA. 

When external auditors request documentation, the company can immediately present an up-to-date record of all models.

Best practices or key components

• Centralized catalog: Keep a single, accessible list of all AI models across departments, including experimental and legacy models.

• Detailed metadata: Record essential details like model owner, purpose, input data, training history, deployment status, and risk level.

• Lifecycle tracking: Document each model’s development, testing, deployment, monitoring, and retirement stages.

• Compliance tagging: Label models based on applicable regulations (e.g., EU AI Act, ISO 42001) to ease compliance tracking.

• Risk scoring: Assess and update risk scores regularly based on model behavior, use case, and impact.

FAQ

What information should be included in an AI model inventory?

A complete inventory should capture the model name, purpose, owner, input and training data, development status, deployment environment, regulatory requirements, risk classification, and monitoring status.

Who should be responsible for maintaining the AI model inventory?

Typically, governance, risk, and compliance (GRC) teams or AI governance officers are responsible for maintaining the inventory, but model owners and developers should contribute updates regularly.

How often should the AI model inventory be updated?

The inventory should be updated continuously, with reviews scheduled at key milestones such as model deployment, significant retraining events, regulatory updates, or at least quarterly.

Is an AI model inventory required by law?

Certain regulations like the EU AI Act and frameworks like ISO 42001 recommend or require maintaining a model inventory, especially for high-risk AI systems. Even when not legally mandated, it is a best practice.