Introduction
The increased false positive rate in Darktrace's Industrial Immune System when monitoring OT networks is a critical issue that demands immediate attention. This problem could significantly impact the effectiveness of our cybersecurity solution and potentially erode customer trust. I'll approach this analysis systematically, focusing on identifying the root cause, validating hypotheses, and developing both short-term fixes and long-term strategies.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Changes in network structure could affect detection accuracy. Expected answer: Yes, some clients have expanded their OT networks. Impact on approach: If confirmed, we'll need to investigate our system's adaptability to network changes.
Why it matters: Algorithm changes could directly impact false positive rates. Expected answer: Yes, there was a major update two months ago. Impact on approach: If confirmed, we'll need to review the update's impact on detection accuracy.
Why it matters: Training data quality directly affects detection accuracy. Expected answer: No significant changes in training data. Impact on approach: If changes occurred, we'd need to reassess our data collection and preprocessing methods.
Why it matters: Compliance changes could force adjustments in our system's sensitivity. Expected answer: No major regulatory changes. Impact on approach: If changes occurred, we'd need to review our compliance adaptation process.
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