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EPasskey vent Monitoring provides essential debugging capabilities for large-scale passkey deployments, helping you identify unusual behavior, spot client-side issues, and maintain system health proactively.

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Why Event Monitoring matters

When deploying passkeys at scale, you’ll encounter edge cases that are difficult to predict:
  • OS and Browser Updates: New iOS betas or Android updates can introduce unexpected behaviors
  • Device Manufacturer Variations: Certain Android manufacturers may have unique implementations
  • Third-Party Password Managers: Different password managers behave differently across platforms
  • Regional Variations: Users in different locations may experience unique issues
While device-specific issues may only affect a small fraction of users, they create poor experiences. Event monitoring helps you identify and fix these issues proactively before they impact more users.

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Event Categories

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Expected Events

Normal user behaviors that are part of standard flows:
  • User Cancellations: Users canceling append or login processes
  • Navigation Events: Users clicking “Learn More” or “Manage” buttons
  • Timeout Events: Authentication attempts that exceed time limits
  • User Choice Events: Users selecting different authentication options

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Unexpected Events

Issues requiring investigation and potential action:
  • Unclassified Errors: New error patterns not yet categorized
  • Client-Side Failures: Device or browser-specific issues
  • Integration Problems: Issues with third-party services or password managers
  • Anomalous Patterns: Unusual spikes or changes in error rates
Focus on the unexpected events chart. Steep increases in any category typically indicate an emerging issue that needs immediate attention.

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Working with Event Monitoring

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Daily Health Checks

Establish a routine for monitoring system health:
  1. Review Event Trends: Check if error categories are increasing or decreasing
  2. Filter by Severity: Focus on statistically significant anomalies
  3. Investigate Spikes: Look for sudden changes in error patterns
  4. Drill Down: Examine specific processes causing issues

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Anomaly Detection

The system uses statistical analysis to identify significant issues:
  • Z-Score Calculation: Automatically calculates statistical significance
  • Severity Thresholds: Filter events by statistical significance levels
  • Pattern Recognition: Identifies recurring issues across users
  • Trend Analysis: Tracks whether issues are increasing or decreasing
The Z-score helps prioritize which anomalies need immediate attention. Lower Z-scores indicate more significant deviations from normal patterns.

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Pattern Management

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Creating Custom Patterns

Define patterns to categorize and track specific issues:
1

Identify the Pattern

Find recurring error messages or behaviors in your event logs.
2

Create Pattern Definition

Provide a name, description, and regex pattern to match the events.
3

Assign Priority

Set priority levels based on user impact:
  • High Priority: Append cancellations or login failures
  • Medium Priority: Navigation events like “Learn More” clicks
  • Low Priority: Informational events
4

Monitor and Adjust

Track how often the pattern occurs and adjust priority as needed.

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Best Practices

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Proactive Monitoring

  • Daily Reviews: Check event monitoring at least once daily
  • Set Baselines: Understand normal error rates for your system
  • Track Trends: Monitor how error rates change over time
  • Document Patterns: Keep records of resolved issues for future reference

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Issue Prevention

  • Early Detection: Address issues before users report them
  • Pattern Recognition: Use historical data to predict potential problems
  • Testing Coverage: Focus testing on problematic device/browser combinations
  • User Communication: Proactively inform users about known issues and workarounds
Android fragmentation and third-party password managers are common sources of edge cases. Pay special attention to errors from these sources.