> ## Documentation Index
> Fetch the complete documentation index at: https://docs.corbado.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Passkey Event Monitoring

> Proactively identify and debug passkey authentication issues at scale with comprehensive event monitoring and anomaly detection.

**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

<Warning>
  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.
</Warning>

## Event Categories

### 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

### 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

<Tip>
  Focus on the unexpected events chart. Steep increases in any category typically indicate an emerging issue that needs immediate attention.
</Tip>

## Working with Event Monitoring

### 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

### 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

<Info>
  The Z-score helps prioritize which anomalies need immediate attention. Lower Z-scores indicate more significant deviations from normal patterns.
</Info>

## Pattern Management

### Creating Custom Patterns

Define patterns to categorize and track specific issues:

<Steps>
  <Step title="Identify the Pattern">
    Find recurring error messages or behaviors in your event logs.
  </Step>

  <Step title="Create Pattern Definition">
    Provide a name, description, and regex pattern to match the events.
  </Step>

  <Step title="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
  </Step>

  <Step title="Monitor and Adjust">
    Track how often the pattern occurs and adjust priority as needed.
  </Step>
</Steps>

## Best Practices

### 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

### 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

<Warning>
  Android fragmentation and third-party password managers are common sources of edge cases. Pay special attention to errors from these sources.
</Warning>