Why do precision and recall need to be balanced when evaluating an ML model?

Why This Matters for Security Teams

precision and recall are not just model-scoring concepts. They describe different operational risks: precision measures how often positive predictions are right, while recall measures how many true positives the model finds. In security, those failure modes map directly to false alarms and missed detections, which is why an imbalanced model can look effective in testing and still fail in production.

For teams using ML in detection, classification, or ranking workflows, the real question is which error is more costly in context. A high-precision model may miss too many threats, while a high-recall model may overwhelm analysts with noise. Current guidance from NIST SP 800-53 Rev 5 Security and Privacy Controls reinforces the need for control objectives that are both effective and operationally sustainable, not merely accurate on paper. NHI Management Group also shows how high-impact failure modes are often hidden until incident response, as seen in the Ultimate Guide to NHIs and the Hugging Face Spaces breach.

In practice, many security teams discover the cost of poor balance only after false negatives have allowed abuse or false positives have burned analyst time to the point that alerts stop being trusted.

How It Works in Practice

Balancing precision and recall starts with the use case, not the metric. If the model supports fraud blocking, malware detection, or account compromise detection, recall may need to be prioritised because missed positives are expensive. If it drives automated enforcement, precision may matter more because a false positive can interrupt legitimate work. In mature environments, teams define an acceptable operating point by comparing the business cost of each error type rather than chasing the highest score.

Practitioners usually evaluate models with a precision-recall curve, then choose the threshold that matches the risk profile. The F1 score is useful when both error types matter and no single metric should dominate. However, F1 is still a summary, not a decision rule. For some workflows, a weighted F-beta score is more appropriate when one failure mode is clearly worse than the other.

• Use precision when false positives trigger costly manual review or automated blocking.
• Use recall when missing a true event creates security exposure or compliance risk.
• Use threshold tuning to align model outputs with the actual control objective.
• Use confusion matrices and per-class metrics so minority-class failures do not get hidden.

This is especially important for NHI-related detection, where compromised secrets, service accounts, and API keys can move quickly across systems; NHI Mgmt Group notes in the Ultimate Guide to NHIs that 80% of identity breaches involved compromised non-human identities. When an ML model is used to flag anomalous identity behaviour, the tolerance for missed detections is usually low, but the tolerance for noisy alerts is not unlimited. These controls tend to break down when data is highly imbalanced and the positive class is rare because headline metrics can stay deceptively strong while the model fails on the cases that matter most.

Common Variations and Edge Cases

Tighter recall often increases review burden, requiring organisations to balance threat coverage against analyst capacity. That tradeoff becomes sharper in regulated or high-volume environments, where even small threshold changes can create large operational effects.

There is no universal standard for this yet, and best practice is evolving. Some teams optimise for precision at the first line of defence and rely on downstream correlation to recover missed cases. Others intentionally accept more false positives in exchange for stronger coverage during high-risk periods. The right balance also changes by class: a rare but severe attack pattern may justify lower precision than a routine classification task.

Two edge cases matter often. First, highly imbalanced datasets can make accuracy meaningless, so precision and recall should be paired with class prevalence and calibration checks. Second, model drift can shift the balance over time, especially when attacker behaviour changes or operational data changes. That is why ongoing evaluation matters more than a one-time benchmark. In practice, teams that do not revisit thresholds after deployment often discover that the metric balance no longer reflects the actual risk profile.

Related resources from NHI Mgmt Group

• Why do precision and recall create blind spots in scanner selection?
• How do teams decide between ROC and precision-recall curves?
• What breaks when ML systems deserialize untrusted model files?
• Should organisations prioritise recall or precision when choosing a SAST platform?