What's causing the increased false positive rate in Darktrace's Antigena Email product over the past week?

Introduction

The increased false positive rate in Darktrace's Antigena Email product over the past week is a critical issue that demands immediate attention. As we analyze this product problem, we'll follow a systematic framework to identify, validate, and address the root cause while considering both immediate and long-term implications.

I'll approach this issue by first clarifying the context, then ruling out external factors before diving deep into the product's functionality and user journey. We'll break down the metric, gather relevant data, form hypotheses, and conduct a thorough root cause analysis. Finally, we'll develop a comprehensive plan to validate our findings and implement solutions.

Step 1

Clarifying Questions (3 minutes)

• Given the sudden increase, I'm wondering about recent changes. Have there been any updates to the Antigena Email product or its underlying systems in the past two weeks?

Why it matters: Recent changes often correlate with performance shifts. Expected answer: Yes, a minor update was deployed last week. Impact on approach: If confirmed, we'd focus on the update's specifics and potential unintended consequences.

• Considering the nature of email security, I'm curious about the current false positive baseline. What's the typical false positive rate for Antigena Email, and how much has it increased?

Why it matters: Understanding the magnitude of the increase helps prioritize the issue. Expected answer: Usually around 0.1%, now it's at 0.5%. Impact on approach: A significant increase would suggest a systemic issue rather than normal fluctuations.

• Thinking about user segments, I'm wondering if this is widespread. Are all customers experiencing increased false positives, or is it limited to specific user groups or email types?

Why it matters: Segmentation can reveal patterns and narrow down potential causes. Expected answer: It's affecting enterprise customers more than SMBs. Impact on approach: If segmented, we'd focus on enterprise-specific factors or configurations.

• Reflecting on the product's core functionality, I'm curious about the detection algorithms. Has there been any recent training or adjustment to the machine learning models used in email classification?

Why it matters: ML model changes can significantly impact false positive rates. Expected answer: The model was retrained two weeks ago with new data. Impact on approach: If confirmed, we'd investigate the new training data and model performance metrics.