Context

CodeShield runs a static application security testing (SAST) model that classifies code findings as either actionable vulnerabilities or benign findings. Security engineers report that too many alerts are false positives, causing developers to ignore the tool and delaying releases.

Current Performance

The model was evaluated on a labeled validation set of 12,000 findings from Java, Python, and JavaScript repositories.

The Problem

The security team values high recall because missed vulnerabilities are costly, but the current false positive volume is overwhelming triage capacity and reducing trust in the tool. You need to evaluate whether the model is acceptable, diagnose where false positives are concentrated, and recommend how to reduce them without sharply increasing false negatives.

Requirements

1. Interpret the current metrics and explain what they imply about false positives.
2. Use the confusion matrix to quantify operational and business impact.
3. Identify likely root causes for the false positive rate across code patterns or languages.
4. Recommend metric-driven changes to thresholding, evaluation, or model design.
5. Explain what additional validation you would run before deployment.

Constraints

• Security review team can manually inspect at most 900 alerts per week.
• Missing a true vulnerability is estimated to cost $4,000 on average.
• Reviewing a false positive costs about $18 in engineer time and creates release friction.
• The tool must support all three languages in a single deployment.