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Error Capture and Monitoring Policy

Policy Status: Draft

This policy is currently draft.

Purpose

To establish comprehensive error capture, logging, and monitoring practices that enable rapid detection, analysis, and resolution of operational issues while maintaining visibility into system health and application performance. This policy ensures that all operational errors are captured, tracked, and addressed in a systematic manner.

Scope

This policy applies to all Acme Corp applications, systems, services, and infrastructure components including: - Web applications and APIs - Backend services and microservices - Databases and data stores - Cloud infrastructure and services - Third-party integrations - Client-side applications - Mobile applications - Scheduled jobs and background processes

Policy Statement

Error Capture Requirements

All applications and systems must implement comprehensive error capture:

  • Application Errors: Capture all unhandled exceptions, runtime errors, and application failures
  • System Errors: Monitor and log operating system errors, service failures, and infrastructure issues
  • Integration Errors: Track failures in third-party API calls, webhooks, and external service integrations
  • User-Facing Errors: Capture client-side errors and failed user interactions
  • Background Process Errors: Monitor scheduled jobs, queue processing, and automated task failures
  • Database Errors: Log query failures, connection issues, and constraint violations
  • Security Events: Capture authentication failures, authorization violations, and suspicious activities

Error Logging Standards

All error logs must include:

  • Timestamp: Precise date and time of error occurrence (UTC)
  • Error Level: Severity classification (Critical, Error, Warning, Info, Debug)
  • Error Message: Human-readable description of what went wrong
  • Stack Trace: Complete call stack for debugging (when applicable)
  • Context: Relevant context including user ID, session, request ID, affected resources
  • Environment: System environment (production, staging, development)
  • Application Version: Software version where error occurred
  • Additional Metadata: Request parameters, state information, correlation IDs

Error Severity Levels

Errors are classified into severity levels:

Critical: - Complete system or service outage - Data loss or corruption - Security breaches or vulnerabilities being exploited - Payment processing failures - Compliance violations

Error: - Feature or functionality failures affecting users - Failed integrations impacting operations - Database connection failures - Failed critical background jobs - Unhandled exceptions in core workflows

Warning: - Degraded performance outside normal parameters - Failed non-critical integrations - Deprecated functionality usage - Approaching resource limits - Retry-able failures

Info: - Normal operational events - Successful critical operations - User actions requiring audit trail - Configuration changes

Debug: - Detailed diagnostic information - Development and troubleshooting data - Performance profiling information

Alerting and Notification (Phase 1)

Current Approach: - All operational errors (Critical, Error, Warning levels) are captured and alerted to Slack channel #ops-alerts - No severity filtering applied initially to maximize visibility - Real-time notification for all captured errors - Objective: Establish complete visibility before optimizing

Monitoring Cadence: - Real-time monitoring of #ops-alerts channel during business hours - Daily review of overnight error accumulation - Weekly error trend analysis - Monthly comprehensive review of error patterns and types

Error Analysis and Pattern Detection

Monthly Review Process: - Analyze error frequency, types, and patterns - Identify recurring issues requiring permanent fixes - Determine if severity-based filtering needed - Assess alert fatigue and notification effectiveness - Review false positives and adjust detection rules

Pattern Identification: - Group similar errors for root cause analysis - Track error trends over time - Identify correlations between errors and deployments - Monitor error rate changes and anomalies

Error Retention and Storage

  • Production Errors: Retain for minimum 90 days
  • Critical Errors: Retain for 1 year
  • Security-Related Errors: Retain for 1 year (per COMP-002)
  • Aggregated Metrics: Retain for 2 years
  • Archived Logs: Move to long-term storage after retention period

Privacy and Data Protection

Error logs must protect sensitive information:

  • PII Redaction: Automatically redact personally identifiable information
  • Credential Masking: Mask passwords, API keys, tokens, and secrets
  • Data Minimization: Log only necessary information for debugging
  • Access Control: Restrict error log access to authorized personnel only
  • Secure Storage: Encrypt error logs at rest and in transit

Roles and Responsibilities

Role Responsibility
IT Operations Manager Oversee error monitoring program, review monthly error analysis
DevOps Team Implement error capture, configure alerting, maintain monitoring tools
Development Team Instrument code with proper error handling and logging
On-Call Engineers Monitor and respond to error alerts, triage and escalate issues
IT Team Members Review #ops-alerts, investigate errors relevant to their systems
Security Team Review security-related errors, investigate suspicious patterns
CTO Review error trends, approve changes to error monitoring approach

Procedures

Implementing Error Capture

1. Code Instrumentation

  1. Implement try-catch blocks around error-prone operations
  2. Use appropriate error logging frameworks (e.g., Winston, Log4j, Sentry)
  3. Add contextual information to error logs
  4. Set appropriate error severity levels

2. Configure Error Tracking

  1. Set up error monitoring service (e.g., Sentry, Rollbar, CloudWatch)
  2. Configure error grouping and deduplication
  3. Set up source maps for client-side error tracking
  4. Enable breadcrumb capture for context

3. Integrate with Slack

  1. Configure webhook integration to #ops-alerts channel
  2. Set up error formatting for readable notifications
  3. Include error details, affected service, and severity
  4. Add links to detailed error information and dashboards

4. Testing

  1. Verify error capture in development environment
  2. Test alert delivery to Slack
  3. Validate error log format and completeness
  4. Ensure sensitive data properly redacted

Error Response and Triage

5. Initial Alert Receipt

  1. Acknowledge error notification in Slack (use emoji reaction)
  2. Assess severity and immediate impact
  3. Determine if immediate action required

6. Investigation

  1. Review error details, stack trace, and context
  2. Check for related errors or patterns
  3. Review recent deployments or changes
  4. Assess number of affected users/systems

7. Categorization

  1. Immediate Action Required: Critical errors affecting users or security
  2. Investigate Further: Errors needing deeper analysis
  3. Known Issue: Errors related to existing tickets
  4. Expected/Acceptable: Errors from expected conditions (e.g., invalid user input)
  5. False Positive: Errors that should be filtered or ignored

8. Resolution

  1. Create incident ticket for critical issues
  2. Add to backlog for non-critical issues
  3. Document investigation findings
  4. Implement fix and verify resolution
  5. Update error capture rules if needed

Monthly Error Review

Conducted first Monday of each month:

9. Data Collection

  1. Export error data from previous month
  2. Generate error frequency reports
  3. Create error distribution charts by type and severity

10. Analysis

  1. Identify top 10 most frequent errors
  2. Analyze error trends compared to previous months
  3. Review critical and unresolved errors
  4. Assess alert volume and team response time

11. Pattern Detection

  1. Group similar errors for root cause analysis
  2. Identify errors caused by recent changes
  3. Find errors that should be elevated in priority
  4. Discover errors that can be filtered as noise

12. Optimization Decisions

  1. Determine if severity filtering should be implemented
  2. Decide if any error categories should be excluded from alerts
  3. Identify errors requiring permanent fixes vs. acceptable noise
  4. Update alerting rules based on findings

13. Documentation

  1. Document review findings and decisions
  2. Update error handling procedures
  3. Create action items for recurring issues
  4. Share summary with team

Error Reporting and Metrics

Weekly metrics reported to IT leadership:

  • Total error count by severity
  • New vs. recurring errors
  • Error rate per application/service
  • Mean time to detection (MTTD)
  • Mean time to resolution (MTTR)
  • Error trends and patterns
  • Top error sources

Exceptions

Exceptions to error capture requirements:

  • Development/Test Environments: May use less comprehensive error capture
  • Expected Errors: Known, acceptable errors (e.g., user input validation) may be filtered after monthly review
  • Third-Party Systems: Errors from external systems we don't control may have limited capture capability
  • Legacy Systems: Systems being decommissioned may have reduced error monitoring

All exceptions must be: - Documented with justification - Reviewed quarterly for continued applicability - Approved by IT Operations Manager

Compliance and Enforcement

  • Code Reviews: All code changes reviewed for proper error handling
  • Automated Testing: Tests must include error condition coverage
  • Deployment Checks: Verify error monitoring active before production deployment
  • Monthly Audits: Review error capture coverage across all systems
  • Metrics Monitoring: Track error capture compliance percentage
  • Continuous Improvement: Regular optimization of error handling based on monthly reviews
  • Training: Annual training on error handling best practices

References

  • Twelve-Factor App Methodology - Logs
  • OWASP Logging Cheat Sheet
  • NIST SP 800-92: Guide to Computer Security Log Management
  • SOC 2 Trust Service Criteria: Monitoring Controls
  • Google SRE Book - Monitoring Distributed Systems

Revision History

Version Date Author Changes
1.0 2025-11-08 IT Team Initial version migrated from Notion with Phase 1 approach

Document Control - Classification: Internal - Distribution: IT team, development team, operations team - Storage: GitHub repository - policy-repository