Use Case Intake

Expanded Definition

Use case intake is the front-end governance step that captures an AI proposal before build or deployment. It turns a vague idea into a reviewable record: what the system is meant to do, who will use it, what data it will touch, what decisions it may influence, and which risk signals deserve scrutiny.

The boundary matters. Intake is not the full approval process, a model inventory entry, or a post-launch monitoring activity. It is the point where organisations decide whether the request is sufficiently described to be assessed, and whether it belongs in a low-risk path, a higher-scrutiny review, or a rejection queue. In practice, weak intake often shows up as missing purpose statements, unclear ownership, or “AI” requests that do not explain the actual decision or workflow.

There is broad consensus that intake should support governance, but implementation detail varies. Some organisations treat it as a lightweight triage form; others use it as a formal gateway for policy, privacy, security, and legal review. The common denominator is that intake must produce enough structure for the next control decision to be meaningful.

Examples and Use Cases

Use case intake appears anywhere an organisation needs to screen AI ideas before they become systems, tools, or embedded workflow steps. It is especially important when the proposal could affect people, operational decisions, or regulated data.

• A business unit submits a request for an internal assistant that drafts customer replies, and intake records the use, audience, and approval owner.
• A product team proposes a scoring model for prioritising service requests, and intake captures the decision impact, data sources, and human review expectations.
• A procurement team wants to deploy an external AI service, and intake documents whether the service will process confidential, personal, or operational data.
• A security team receives a proposal for an autonomous workflow agent, and intake records tool access, escalation paths, and whether the agent can act without direct approval.
• A compliance group reviews a high-visibility AI feature, and intake helps route the case to privacy, risk, legal, and technical reviewers in the right sequence.

The trade-off is speed versus completeness. A minimal intake form can reduce friction, but if it omits intended use, data classes, or ownership, downstream reviewers have to reconstruct the basics and the governance value drops sharply.

Security Implications

When intake is vague or skipped, organisations often approve AI activity without understanding the real exposure. That can lead to the wrong control path, such as treating a high-impact use case like a simple productivity tool, or allowing sensitive data to flow into a system that was never reviewed for that purpose.

Security problems usually emerge through misclassification. If the intake record does not describe the business decision, user population, model boundary, and data sensitivity, reviewers may miss confidentiality risks, privilege issues, unsafe automation, or hidden dependencies on third-party systems. The result is not just incomplete paperwork; it is a failure to route the request to the controls that would have constrained it.

A common practitioner observation is that intake quality predicts review quality. If the first submission is ambiguous, the rest of the governance process tends to become reactive, because reviewers are forced to infer rather than assess. For that reason, intake is often where the earliest and cheapest risk reduction happens.

Domain and Governance Relevance

Use case intake matters because it is the point where AI governance becomes operational. The request is still fluid, so teams can shape scope, ownership, data handling, and review depth before commitments harden into implementation choices. That makes intake a control point, not just an admin form.

In AI governance, intake also creates traceability. It provides the record that links a proposed use case to risk classification, required approvals, and any conditions for proceeding. Without that record, organisations struggle to explain why one request advanced and another was paused.

For NHIMG, the identity connection is indirect but important: the intake stage is where an organisation can detect whether a proposed AI system will rely on service accounts, API keys, delegated access, or agent-like execution. If those elements are not surfaced early, machine access and ownership issues may not be governed until after deployment, when remediation is more expensive.

Related resources from NHI Mgmt Group

• How do IAM teams decide whether an AI use case needs new controls or better NHI hygiene?
• How do organisations decide whether encrypted computation is enough for a use case?
• How do security teams decide whether biometrics are appropriate for a use case?
• How should organisations centralise AI use case and model inventories?