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Identifying AI Assets: Inventory, Data Gathering, and Documentation
You cannot audit what you cannot see, which makes asset identification the very first step in audit planning. This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series walks through why intelligent systems are uniquely hard to catalog, how to structure a comprehensive inventory, what data-gathering methods auditors should expect to review, and how the pervasive risk of shadow AI shapes the discovery effort across the enterprise.
What this episode covers
- Why AI assets are different from traditional IT — multiple owners, datasets, versions, algorithms, licenses, and regulatory frameworks per solution.
- Who leads discovery — data management or AI operations runs the effort while internal audit independently reviews the work.
- Where to start — using the organization’s AI usage policy, baseline inventories, and the model catalog for custom-built products.
- Building a non-punitive inventory — leadership communication, mandated disclosure, and guaranteed protection from reprisal.
- Data-gathering methods — formal project documentation, dataflow diagrams, metadata, and access management systems.
- The ten mandatory data fields auditors expect to see captured consistently for every tool in scope.
- Surveys and interviews — anonymous-but-accountable surveys with quantifiable questions, plus standardized live interviews.
- The shadow AI challenge — decentralized ownership when departments buy tools outside central IT.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
Why are AI assets harder to identify than traditional IT assets?
A traditional program is like a simple hammer that does one job and is easy to track, while an AI solution is like a massive automated manufacturing plant of interconnected systems. A single AI solution may have several owners, rely on multiple underlying models, exist in many software versions at once, require workflow mappings, contain separate training and production datasets, use multiple algorithms, incorporate third-party tools, and need multiple legal licenses and regulatory frameworks.
Should internal audit lead the AI asset discovery effort?
No. Internal audit should never lead or manage the discovery effort. The department responsible for data management or AI operations should lead, while the auditor’s job is to independently review their work. Finding what tools are running requires a structured, team-based approach combining the risk management, operations, and audit teams.
What ten data fields should an AI inventory capture for every tool?
For every tool you should capture the name of the solution, the exact version number, any required licenses, the financial cost, the deployment method, the business purpose, the frequency of use, the relevant stakeholders, the accountable organization owner, and all third-party vendor details. Using a standardized list of fields guarantees consistent, high-quality data.
What is shadow AI and why does it make inventory difficult?
Shadow AI occurs when a department purchases and uses software without ever involving the central technology team, such as a marketing team buying a cloud video editing tool that analyzes customer faces and paying with a corporate credit card. Because these tools are so accessible, ownership becomes highly decentralized and IT may not even know they exist, which makes the auditor’s role in reviewing the discovery process vital.
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Reference: This article is based on concepts discussed in Identifying AI Assets: Inventory, Data Gathering & Documentation.