Category: Model Lifecycle & MLOps

Model Inventory

Also known as: Model Repository, Model Catalog

Simply put

A model inventory is a centralized record that lists the AI and decision models an organization uses, so that the organization has a single, organized view of what models exist at any given time. It functions as a catalog that helps teams track and oversee their models rather than losing sight of them across departments. In practice, many inventories are simple, manually maintained lists that can be harder to keep complete and current than the concept suggests.

Formal definition

A model inventory is a centralized repository that catalogs the AI and decision models across an organization, providing an overview of all models available at a given time and, in more mature implementations, real-time visibility into each model. It is a foundational control in model risk management practice, supporting the identification and oversight of models in use. Note that 'inventory model' in economics and operations research refers to a distinct, unrelated concept (mathematical frameworks for determining optimal order timing and stock quantities) and should not be conflated with a model inventory in the AI governance and model risk sense. As commonly implemented, many model inventories are manually maintained lists (for example, in spreadsheets), which can limit completeness and accuracy; the specific scope, required attributes, and governance obligations attached to an inventory typically vary by organization and regulatory context and are not defined uniformly across frameworks.

Why it matters

A model inventory is often described as a foundational control in model risk management because oversight begins with knowing what models exist. An organization cannot validate, monitor, or govern a model it has not identified, and models frequently proliferate across departments—sometimes in spreadsheets or business-line tools—without central awareness. A complete and current inventory gives risk, compliance, and audit functions a single organized view of the models in use, which supports downstream activities such as validation scheduling, risk tiering, and change tracking.

The practical challenge is that the concept is far simpler than its execution. As commonly implemented, many model inventories are manually maintained lists, often in Excel, which can make them difficult to keep complete and accurate as models are added, retired, or modified. An inventory that lags reality can create a false sense of coverage: models absent from the record are also absent from oversight, undermining the very control the inventory is meant to provide.

Because the specific scope, required attributes, and governance obligations attached to an inventory typically vary by organization and regulatory context, an inventory should be understood as a mechanism that supports and enables risk management rather than one that eliminates model risk on its own. It is a starting point for oversight, not a substitute for validation, monitoring, or the broader control environment.

Who it's relevant to

Model Risk Managers

For those responsible for identifying, measuring, and controlling model risk, the inventory is a foundational control that establishes the population of models subject to oversight. It underpins activities such as risk tiering and validation planning, and its completeness directly affects the coverage of those activities.

Compliance and Audit Functions

Compliance officers and auditors rely on the inventory as evidence of what models an organization is tracking and overseeing. A current, complete inventory supports assurance work, while gaps between the inventory and models actually in use are a common area of scrutiny.

Data Scientists and Model Owners

Those who build and operate models contribute to and depend on the inventory to register new models, record changes, and retire models no longer in use. Because many inventories are manually maintained, the accuracy of the record depends substantially on these teams' ongoing input.

AI Governance and Oversight Bodies

Groups responsible for organizational oversight of AI systems use the inventory to maintain a single view of models in use across departments. Note that the scope and governance obligations attached to an inventory are not defined uniformly across frameworks and typically depend on organizational policy and regulatory context.

Inside Model Inventory

Model identification and metadata

A unique identifier for each model along with descriptive attributes such as model name, version, purpose, and business function, allowing the model to be tracked distinctly over its lifecycle.

Ownership and accountability records

Documentation of the roles responsible for the model, which in many frameworks maps to lines of defense—for example the model owner or developer (commonly first line) and independent validation or oversight functions (commonly second line). This supports AI governance accountability rather than measuring model risk directly.

Risk classification or tiering

An assigned risk rating or tier for each model, often reflecting factors such as materiality, complexity, and use. The distinction between inherent risk (before controls) and residual risk (after controls) may be captured separately where a framework requires it, though practices vary.

Lifecycle and status information

The model's current stage—such as development, in validation, approved for use, in production, or retired—together with relevant dates. What constitutes each status can differ by organization and by governing framework.

Validation and review history

Records of validation activities, review dates, and outcomes. This typically references validation (assessing whether the model is fit for its intended purpose) and should be kept distinct from ongoing performance monitoring, which addresses potential model performance degradation over time.

Dependencies and interconnections

Information on upstream data sources, downstream systems, and relationships to other models, so that changes or issues in one model can be traced to those it affects.

Documentation and control references

Links or references to supporting documentation such as model development documentation, validation reports, limitations, and approved conditions of use, supporting both governance oversight and risk management activities.

Common questions

Answers to the questions practitioners most commonly ask about Model Inventory.

Is a model inventory just a list of the models an organization has deployed?

Not quite. While a model inventory is often described as a comprehensive record of an organization's models, treating it as a simple deployment list understates its purpose. In many model risk management frameworks it is expected to capture more than the fact that a model exists, including attributes needed to identify, measure, and monitor model risk. Reducing it to a deployment list is a common error, and the specific scope of required attributes varies by framework and by sector.

Does maintaining a model inventory mean an organization has satisfied its model risk management obligations?

No. A model inventory is typically treated as a foundational component that supports model risk management, not a substitute for it. Inventorying a model does not by itself validate it, monitor its performance, or control its risk. Professionals frequently err by treating inventory completeness as evidence of adequate oversight; the inventory is a tool that enables governance and risk activities rather than a control that reduces risk on its own.

What should be considered a 'model' for the purpose of inclusion in the inventory?

Scoping decisions are a common practical challenge, because definitions of what constitutes a model vary across frameworks and sectors. Organizations generally need to establish their own documented criteria for what qualifies, and to apply those criteria consistently. Where a tool, calculation, or automated process falls near the boundary of a model definition, the treatment can be contested, and different frameworks may reach different conclusions. This entry does not prescribe a single universal definition.

Who is typically responsible for maintaining the model inventory?

Responsibility for the inventory commonly involves more than one line of defense, and the specific allocation depends on an organization's governance structure. Ownership of individual entries, oversight of the inventory as a whole, and independent review may be assigned to different functions. Because the first, second, and third lines of defense serve distinct roles, care should be taken not to blur responsibility for populating entries with responsibility for challenging or reviewing them.

How often should a model inventory be updated?

Update frequency depends on organizational policy, the pace of change in the model landscape, and any applicable framework expectations. Rather than a fixed universal interval, inventories are often maintained through defined triggers, such as new deployments, material changes to existing models, decommissioning, and periodic review cycles. The appropriate cadence should be documented and matched to the rate at which relevant information changes.

What attributes are commonly recorded for each model in the inventory?

The attributes captured vary by framework and by an organization's own policy, so this entry does not present a definitive list. In many implementations, recorded information may relate to identification, ownership, purpose or use, lifecycle status, and links to associated risk and validation activities. Organizations typically tailor the attribute set to support their identification, measurement, monitoring, and control needs, and to remain consistent with any applicable guidance or standards in their jurisdiction.

Common misconceptions

A model inventory is only a compliance checklist that satisfies a regulator once completed.

A model inventory is more accurately understood as an ongoing governance and risk-management tool. Its value depends on being maintained continuously as models change, are added, or are retired; treating it as a one-time deliverable undermines its purpose. It supports oversight but does not by itself eliminate model risk.

Everything an organization labels 'AI' or 'a model' has a single agreed definition that determines what belongs in the inventory.

What qualifies as a 'model' varies by framework and sector. In banking model risk contexts, definitions have historically been framed by guidance such as SR 11-7, while general enterprise AI governance may scope items differently. The boundary of what to include is often a policy decision the organization must make and document, not a universally settled classification.

Listing a model in the inventory demonstrates that the model has been validated and is performing well.

An inventory records the existence, status, and attributes of models; it does not itself validate them or confirm performance. Validation (fitness for intended purpose) and ongoing performance monitoring (detecting degradation) are distinct activities whose results the inventory may reference but does not replace.

Best practices

Define and document the organization's criteria for what constitutes a 'model' before populating the inventory, recognizing that this scope can differ across sectors and frameworks and should be recorded explicitly.

Assign a clear owner and accountable roles for each model, and map these to your organization's lines of defense so that development, oversight, and independent review responsibilities remain distinct.

Maintain the inventory as a living record with defined processes for adding, updating, and retiring models, rather than treating it as a static or one-time compliance artifact.

Capture risk classification and, where your framework calls for it, distinguish inherent risk from residual risk so that tiering reflects both the model's exposure and the effect of applied controls.

Link inventory entries to supporting documentation—including validation reports, stated limitations, and approved conditions of use—so that references are traceable and current.

Record dependencies between models, data sources, and downstream systems to enable impact analysis when changes occur, keeping validation history separate from ongoing performance monitoring records.