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. Also include version information, last review date, approval records, known limitations, and links to detailed documentation. For vendor-provided models, include contract details, vendor contact, and SLA terms. The inventory should answer "what AI do we have and how is it being used?"

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. Define clear processes for adding new models, updating existing entries, and retiring deprecated models. Automated integrations with development and deployment pipelines reduce manual burden. Regular audits verify inventory completeness and accuracy.

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. Trigger-based updates ensure timely capture of changes. Stale inventories lose value quickly as AI portfolios evolve. Consider automated synchronization with model registries and deployment systems to maintain currency without manual effort.

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. The EU AI Act requires providers to maintain technical documentation and deployers to keep logs—both are supported by comprehensive inventories. Customer contracts and insurance policies may also require inventory capabilities.

How do you discover AI models that aren't in the inventory?

Shadow AI—models deployed outside official processes—is a growing concern. Conduct periodic discovery audits across cloud platforms, on-premise systems, and third-party integrations. Monitor procurement and vendor management processes for AI purchases. Create easy processes for teams to register models, reducing incentives to bypass governance. Establish clear policies requiring inventory registration before deployment. Education helps teams understand why inventory matters.

Should the inventory include experimental and prototype models?

Yes, but with appropriate categorization. Even experimental models may access sensitive data or influence decisions during testing. Track experimental models with lighter-weight metadata requirements. Define clear criteria for when experimental models require full inventory treatment. This approach provides visibility without creating excessive burden on early-stage exploration. Require full inventory entry before any model moves to production.

How do you manage a large AI model inventory?

For organizations with many models, implement tiered governance with different documentation requirements by risk level. Use consistent taxonomies and tagging for searchability. Automate data collection where possible. Create dashboards showing portfolio-level metrics (models by risk level, compliance status, review dates). Regular cleanup removes deprecated entries. Consider dedicated inventory tools rather than spreadsheets for scale.