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Product Management Root Cause Analysis Question: Investigating increased false positives in Darktrace's OT network monitoring

What's causing the increased false positive rate in Darktrace's Industrial Immune System when monitoring OT networks?

Data Analysis Problem Solving Technical Understanding Cybersecurity Industrial Automation Critical Infrastructure
Root Cause Analysis Cybersecurity OT Networks AI Algorithms False Positives

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)

  • Considering the nature of OT networks, I'm wondering about recent changes in network complexity. Have there been any significant expansions or integrations in the monitored OT environments recently?

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.

  • Looking at the timing, I'm thinking about recent updates to our detection algorithms. Have we rolled out any major updates to the Industrial Immune System in the past 3-6 months?

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.

  • Considering the specificity of OT networks, I'm curious about the diversity of our training data. Has there been any change in the composition or sources of data used to train our system recently?

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.

  • Given the critical nature of OT networks, I'm wondering about any changes in regulatory requirements or industry standards. Have there been any new compliance mandates that might have affected our detection thresholds?

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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