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In a security operations center (SOC), an incident response team is tasked with automating the process of identifying and mitigating phishing attacks. They decide to implement a machine learning model that analyzes email metadata and content to classify emails as either benign or malicious. The model is trained on a dataset containing 10,000 emails, of which 2,000 are labeled as phishing. After deployment, the model achieves an accuracy of 90%. However, the team notices that the model has a precision of 70% and a recall of 85%. Given this scenario, what can be inferred about the model\'s performance in identifying phishing emails, and what steps should the team consider to improve its effectiveness?
The model is effective in identifying phishing emails but may misclassify benign emails, indicating a need for better feature selection or additional training data.
The model is highly accurate and does not require any adjustments since the accuracy is above 85%.
The model's recall is too low, suggesting that it fails to identify a significant number of phishing emails, which is acceptable in a production environment.
The model's precision is high, indicating that it rarely misclassifies benign emails, and thus no further action is needed.

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