A security engineer is building an AI-assisted phishing detection pipeline that must process 500,000 emails per day and flag suspicious ones for analyst review. The AI model's false positive rate must be minimized to avoid alert fatigue. Which design consideration is most important to address first?

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A. Tuning the model's decision threshold using a validated representative sample of recent email data to achieve an acceptable precision-recall balance before production deployment. Decision threshold tuning using a representative validation set before deployment is essential for balancing false positive and false negative rates at scale. Alert fatigue from excessive false positives is a known operational risk explicitly addressed in AI-assisted security workflows in the SecAI+ objectives. Reactive threshold adjustment after deployment causes operational disruption. A more complex model may overfit, worsening production performance. Email encryption before inference prevents the model from analyzing content.

A security engineer is building an AI-assisted phishing detection pipeline that must process 500,000 emails per day and flag suspicious ones for analyst review. The AI model's false positive rate must be minimized to avoid alert fatigue. Which design consideration is most important to address first?

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