Use-case selection is the process of choosing the specific business problem an AI system should solve. In security programs, this matters because an AI tool only helps when the task, data sources, success criteria, and controls are clearly defined. A vague use case often leads to wasted spend, poor model performance, and unsafe handling of logs, tickets, or other sensitive data.
In practice, strong use-case selection helps teams focus AI on tasks such as phishing triage, alert prioritization, fraud detection, or malware classification. Weak selection can create blind spots: the system may be applied to a problem that is too broad, too noisy, or too hard to measure, making it easy to overtrust bad outputs. For defenders, the goal is to match the model to a concrete workflow and limit its permissions, inputs, and outputs. For attackers, poorly chosen use cases can expose gaps in governance, enabling confusion, overreach, or misuse of enterprise data.