AI use case inventory

Expanded Definition

An AI use case inventory is not just a catalogue of models. It is an operational register that ties each AI system to business purpose, data inputs, approval status, owning team, risk tier, and external dependencies. In NHI and agentic AI environments, the inventory should also capture whether a use case includes autonomous execution, tool access, or credential use, because those factors change the governance burden materially. This makes the inventory a control surface for visibility, not a one-time discovery exercise.

Definitions vary across vendors on whether an “AI use case” means a business workflow, a model instance, or a deployed agent. NHI Management Group treats the inventory as the broader governance record that connects all three, so security, privacy, and legal teams can assess exposure consistently. That approach aligns well with the NIST Cybersecurity Framework 2.0, which emphasises identifying assets and managing risk across the enterprise.

The most common misapplication is treating the inventory as a spreadsheet of approved chatbots, which occurs when organisations omit embedded agents, vendor-hosted models, and shadow AI integrated through SaaS tools.

Examples and Use Cases

Implementing an AI use case inventory rigorously often introduces governance overhead, requiring organisations to weigh faster experimentation against the cost of review, classification, and ongoing change tracking.

• A procurement team logs a contract-review agent, its source documents, approval owner, and whether it can draft or only summarise, so legal review is proportional to execution authority.
• A security team records an internal copilot that queries ticketing data and secrets stores, then flags the associated risk as higher because data access is broader than the front-end UI suggests.
• An organisation maps a customer-support chatbot to a documented use case, but also links the underlying LLM service, fallback prompts, and escalation path to show how output is governed.
• An incident-response team adds a newly discovered autonomous workflow after reviewing logs from a DeepSeek breach case study, because the event shows how quickly AI exposure can move from development to operational risk.
• A platform team correlates the inventory with NIST Cybersecurity Framework 2.0 asset management activities so new models are reviewed before production access is granted.

For broader context on AI and secrets exposure, NHI Management Group’s DeepSeek breach analysis shows how quickly an AI system can become a security event when its dependencies are not tracked.

Why It Matters in NHI Security

An AI use case inventory matters because non-human identities, tokens, and service accounts are often introduced long before anyone realises a new AI workflow has gone live. Without inventory discipline, organisations cannot tell which agent has access to which secrets, which model is handling regulated data, or which approvals are still valid after a prompt, connector, or vendor update. That is where auditability breaks down.

NHI Management Group research on the DeepSeek breach shows how large AI exposures can surface from weak visibility, and the broader LLMjacking analysis highlights that attackers move fast when NHIs are exposed, with AWS credential access attempts occurring in as little as 9 minutes and an average of 17 minutes in observed cases. A complete inventory helps security teams connect AI behaviour to identity and secret management controls before abuse becomes operational.

Organisations typically encounter the need for an AI use case inventory only after a model leak, shadow deployment, or credential incident, at which point the inventory becomes operationally unavoidable to rebuild trust and contain impact.

Related resources from NHI Mgmt Group

• How do IAM teams decide whether an AI use case needs new controls or better NHI hygiene?
• Asset Inventory
• Secrets Inventory
• What NHI types do Agentic AI systems typically use?