An organization uses an AI-powered tool to detect and respond to cybersecurity threats in real time. An IS auditor finds that the tool produces excessive false positives, increasing the workload of th

Question

Options

• AUse a log analysis tool to examine the types and frequency of alerts generated.
• BImplement a benchmarking tool to compare the system's alerting capability with industry
• CConduct penetration testing to assess the system's ability to detect genuine threats.
• DDeploy a machine learning (ML) validation tool to increase the model's accuracy and performance.

• A
  4% (1)
• B
  24% (6)
• C
  12% (3)
• D
  60% (15)

Explanation

When an AI/ML model produces excessive false positives, the root cause is typically an underperforming or poorly tuned model. Deploying an ML validation tool evaluates model metrics (precision, recall, F1 score, ROC-AUC) and identifies opportunities to retrain, tune hyperparameters, or adjust decision thresholds-directly addressing model accuracy. Option A (log analysis) describes symptoms but does not improve model performance. Option B (benchmarking) compares output but doesn't resolve the underlying accuracy problem. Option C (penetration testing) assesses whether real threats are detected but does not address the false positive rate, which is a model calibration issue.

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