GenAI Use Cases

What GenAI Use Cases Mean in Practice

GenAI use cases are not just a list of prompts or features, they are concrete work patterns that determine where generative AI can safely create value, who owns the output, and which data, permissions, and approval paths the system is allowed to touch.

For security teams, the term matters because the same use case can be low risk in one context and high risk in another. A drafting assistant, a code generator, and a workflow agent may all use the same model, but each has a different exposure profile, especially when they can read internal content or trigger actions.

Where GenAI Use Cases Create Security and Governance Pressure

The security impact comes from the operating context, not from the model alone. A use case that can access regulated data, internal knowledge bases, or business systems needs controls for data minimisation, prompt handling, output review, and tool boundaries.

That is why use-case design should include questions such as what data the system may see, whether outputs are advisory or executable, and whether a human must approve the action. This becomes especially important when the use case includes automation, because decision rights can shift from the user to the system unless the guardrails are explicit.

When teams describe GenAI use cases precisely, they can distinguish between content generation, analysis support, and agentic execution. That distinction drives the right governance model, and it helps prevent a simple productivity pilot from becoming an uncontrolled access path.

Common Patterns in GenAI Use Cases

Most GenAI use cases fall into a few repeatable patterns: chat assistants that answer questions, summarisation tools that condense documents, code assistants that generate or explain software, and workflow assistants that draft, classify, or route work.

In identity and security settings, the more sensitive pattern is the one that reaches beyond conversation. If the use case can query systems, retrieve internal data, or act on behalf of a user, it starts to behave less like a document tool and more like a governed automation layer.

That is why the same architecture may support harmless experimentation in one area and create privilege, data leakage, or approval risks in another. The practical question is not whether GenAI is being used, but which task it is performing and what authority it has while doing it.

How Organisations Should Frame and Approve GenAI Use Cases

Use-case approval works best when organisations treat each GenAI use case as a governed boundary, not a generic platform feature. The approval should record the intended business outcome, the data classes involved, the allowed actions, and the responsible owner.

One useful discipline is to separate read, generate, and act. A system that only drafts text has a different control profile from a system that can trigger tickets, change records, or customer-facing responses. The more the use case moves toward action, the more important it becomes to define review, logging, escalation, and rollback behaviour.

For broader governance context, the AI Governance Guide is the most relevant starting point for linking use cases to inventory, ownership, oversight, and decommissioning decisions.

Risk and Threat Considerations

GenAI use cases can create exposure when they are approved too broadly, connected to sensitive data without clear limits, or allowed to take actions that exceed the user’s intended authority. The biggest risks are data leakage, prompt injection, unauthorized disclosure through outputs, and over-automation of decisions that still need human review.

Failure mechanism: A use case combines broad retrieval, weak output controls, or delegated action rights, so the system can surface sensitive information or execute an unintended step after a malicious or mistaken prompt.

Impact: The result can be confidentiality loss, policy violations, business process abuse, or a trust failure that makes downstream users rely on unsafe outputs or actions.

Practitioner Guidance

Why practitioners should care: A GenAI use case is the point where model capability becomes an operational decision. The control problem is not the model itself, but the exact task it is allowed to perform, the data it may consume, and the authority attached to its output.

Common misunderstanding: Teams often assume that if a use case is helpful, it is automatically low risk. In practice, the same use case can be acceptable for internal drafting but inappropriate for direct action, especially when it touches customer data, code, or privileged workflows.

Practitioner takeaway: Approve GenAI use cases by task, data, and authority, then revisit that approval whenever the use case expands from generating content to triggering decisions or actions.

Related resources from NHI Mgmt Group

• How should security teams govern unstructured data for GenAI use cases?
• Why do traditional endpoint controls fail for SaaS and GenAI use cases?
• How should security teams implement GenAI stress testing across different AI systems and use cases?
• Which controls should enterprises prioritise before scaling GenAI use cases?