Implement Real-Time Anomaly Detection & Alerts

This standard mandates the implementation of real-time anomaly detection and alerts to proactively detect and respond to system failures.

1. Implement Real-Time Anomaly Detection & Alerts:

System failures and degradations must be detected proactively, not reactively. This approach ensures system stability and minimizes downtime.

• 1.1 AI-Driven Anomaly Detection:
  • 1.1.1 Anomaly Detection Tools:
    • Use AI-driven anomaly detection tools (Datadog, Prometheus, AWS CloudWatch) to identify irregularities.
    • Automate the training and deployment of anomaly detection models.
  • 1.1.2 Anomaly Thresholds:
    • Define anomaly thresholds based on historical data and system behaviour.
    • Automate the adjustment of anomaly thresholds.
• 1.2 Automated Alerting:
  • 1.2.1 Alerting Mechanisms:
    • Implement automated alerting with clear escalation paths.
    • Automate the delivery of alerts to relevant teams.
  • 1.2.2 Alert Context:
    • Provide detailed context in alerts to facilitate rapid issue resolution.
    • Automate the inclusion of relevant logs, metrics, and traces in alerts.
• 1.3 Incident Logging and Analysis:
  • 1.3.1 Incident Logging:
    • Ensure all incidents are logged and classified for future prevention.
    • Automate the logging of incident details.
  • 1.3.2 Incident Analysis:
    • Implement automated incident analysis to identify root causes and patterns.
    • Automate the generation of incident analysis reports.

By implementing real-time anomaly detection and alerts, organisations can proactively address system issues and maintain high availability.