Opinion-based selection is a curated choice made through editorial or analyst judgment, rather than a strict scoring model or automated ranking. In practice, the selector uses experience, context, and manual review to decide what belongs on the list and in what order.
In cyber security, this matters because many decisions cannot be reduced to a single metric. Analysts may use opinion-based selection when choosing the most relevant alerts, indicators of compromise, security tools, or remediation steps for a report or dashboard. It also appears in defensive guidance, where experts highlight the controls they consider most useful for a specific threat model. The drawback is subjectivity: different reviewers may choose different items, so the method should be paired with clear criteria, evidence, and transparent assumptions. Used well, it helps teams prioritize what is operationally important rather than what only looks best on paper.