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An AI inventory is the central record of all artificial intelligence systems that exist within an organization. It functions as a single source of truth that governance, compliance, risk, and leadership teams use to understand what AI the organization operates, where it runs, who is responsible for it, and what its current governance status is.
An AI inventory is sometimes referred to as an AI registry or an AI CMDB (AI Configuration Management Database). Regardless of the name, the concept is the same: it is the structured, maintained, and continuously updated record of your organization’s AI portfolio.
A well maintained AI inventory captures more than just a list of names. Each entry is a complete governance record for an AI system. In the Holistic AI platform, this structured record is called an Asset, and its metadata is organized through the Asset Schema.
The information captured for each AI system typically includes:
System Identity: The name of the AI system, what it does, what type of AI it uses (machine learning model, LLM application, multi agent system, etc.), and a description of its purpose within the organization.
Ownership and Stakeholders: Who built the system, who currently maintains it, which team or business unit it belongs to, and who is accountable for its governance. This information can be populated automatically from connected identity systems like Azure, AWS, and Workday.
Technical Profile: What platform the system runs on, what frameworks and libraries it uses, where it is deployed, and what other systems or data sources it connects to.
Risk Profile: What risk level the system has been assigned, which risk dimensions are most relevant to it, and what assessment or testing results are on record.
Compliance Status: Which regulatory frameworks apply to the system (such as the EU AI Act, NIST AI RMF, or ISO 42001), whether required assessments have been completed, and what compliance gaps remain.
Governance History: When the system was first discovered, when it was last assessed, what changes have been made over time, and what remediation actions are currently in progress.
Many organizations begin tracking their AI systems in spreadsheets. While this works when you have a handful of models, it breaks down quickly in any enterprise environment. Spreadsheets depend entirely on people remembering to add new entries, update existing ones, and remove deprecated systems. In practice, this does not happen consistently.
The result is an inventory that is always out of date and always incomplete. Teams deploy new models without logging them. Systems get updated without records being changed. Entire departments adopt AI tools without central teams knowing about it. Within months, the spreadsheet becomes unreliable, and governance teams lose the visibility they need.
An effective AI inventory needs to be automated, connected to your infrastructure, and continuously updated by the same systems that host your AI rather than by human data entry.
In the Holistic AI platform, the AI inventory is called the AI CMDB. It is populated automatically by the IDENTIFY module through the discovery process. As IDENTIFY connects to your code repositories, cloud platforms, ML tools, LLM providers, and enterprise systems, it discovers AI activity and registers each system in the AI CMDB with structured metadata.
The AI CMDB stays current because discovery runs continuously. When a team deploys a new model, connects a new LLM API, or starts a new experiment, the system appears in the inventory automatically. When existing systems are updated or deprecated, those changes are reflected as well.
The AI CMDB also serves as the integration point with your broader enterprise systems. For organizations using ServiceNow, the AI CMDB can synchronize with your existing configuration management database so that AI assets appear alongside your other IT assets in a unified view.
Governance Lead: Views the complete AI portfolio, sets governance priorities, tracks overall governance posture, reports to leadership
Risk Manager: Identifies which systems need risk assessment, prioritizes based on risk level, tracks risk trends over time
Compliance Team: Maps AI systems to applicable regulations, verifies that required assessments are completed, generates audit evidence
CISO / Security: Reviews AI system exposure and security posture, tracks which systems handle sensitive data, monitors for security risks
Data Science Lead: Sees all models their team is responsible for, tracks assessment results, manages remediation for flagged issues
Executive Leadership: Gets visibility into the scale of AI adoption, understands governance maturity, reviews organizational readiness for regulations