AI-Driven Incident Detection & Response

Ensure all services emit metrics, logs, and traces to a unified observability platform. Standardise logging formats and metric naming. Establish baseline performance profiles for all critical services. Map service dependencies. Standardise on OpenTelemetry for instrumentation so you are not locked into a single vendor. Ensure logs include a correlation ID that traces a request across all services. Map service dependencies using automated discovery tools rather than relying on manually maintained architecture diagrams that go stale.

Implement ML-based anomaly detection on key metrics (latency, error rates, throughput, resource utilisation). Use unsupervised learning to establish normal behaviour patterns. Configure seasonal models to handle daily/weekly traffic patterns. Set up intelligent alerting that groups related anomalies. Configure seasonal decomposition for metrics that follow time-of-day and day-of-week patterns; without this, Monday morning traffic spikes will generate false alerts every week. Start with error rate and latency P99 as your primary anomaly signals since these are the strongest predictors of user impact.

Train AI to correlate anomalies across services and identify the originating cause. Use topology-aware analysis that understands service dependencies. Build pattern matching against historical incidents. Generate human-readable explanations of probable root causes. Train the correlation engine on at least 20 historical incidents with documented root causes before expecting reliable suggestions. Weight recent incidents more heavily since your architecture evolves. Include infrastructure events like autoscaling triggers and certificate renewals in the correlation data.

For known incident patterns, build automated runbooks: scale up resources, restart services, roll back deployments, reroute traffic. Start with low-risk remediations and expand as confidence grows. Maintain human approval for high-risk actions. Begin with three safe, high-frequency remediations: restart a crashed pod, scale up a service hitting CPU limits, and roll back a deployment that caused an error spike. Require two-person approval for any remediation that affects a database or stateful service. Log every automated action for audit.

Feed incident post-mortems back into AI models. Build a library of incident patterns and their resolutions. Track MTTR, MTTD (Mean Time to Detect), and false positive rates. Expand auto-remediation coverage as reliability improves. Review false positive rates monthly and retire alert rules that have not fired a true positive in 90 days. Track the ratio of auto-remediated to manually-resolved incidents as your north-star metric. Target 50 percent auto-remediation within 12 months for non-critical incidents.