What caused the sudden spike in false positives for Shape Security's Credential Stuffing Prevention service yesterday?

Introduction

The sudden spike in false positives for Shape Security's Credential Stuffing Prevention service is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term implications for our product strategy.

I'll approach this problem by first clarifying the context, then ruling out external factors before diving deep into our product mechanics, metric analysis, and data-driven hypothesis generation. We'll conclude with a structured plan for validation and resolution.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking this could be related to a recent deployment. Has there been any significant update to the service in the past 24-48 hours?

Why it matters: Recent changes often correlate with sudden performance shifts. Expected answer: Yes, there was a deployment yesterday morning. Impact on approach: If confirmed, we'd focus on changes in that deployment.

• Considering the nature of credential stuffing attacks, I'm curious about the scale. What percentage increase in false positives are we seeing compared to our baseline?

Why it matters: The magnitude helps prioritize our response and narrow down potential causes. Expected answer: A 30-50% increase over normal levels. Impact on approach: A larger spike might indicate a systemic issue rather than an edge case.

• Given that false positives directly impact user experience, I'm wondering about user segments. Are we seeing this spike across all customer types or is it concentrated in specific industries or account sizes?

Why it matters: Segmentation can reveal patterns pointing to specific causes. Expected answer: The spike is more pronounced in enterprise accounts. Impact on approach: We'd investigate enterprise-specific features or configurations.

• Thinking about our detection mechanisms, has there been any change in the volume or pattern of incoming traffic that our service is processing?

Why it matters: Unusual traffic patterns could be overwhelming our systems. Expected answer: Traffic volume is within normal ranges, but patterns have shifted. Impact on approach: We'd focus on analyzing the new traffic patterns and our system's response.