AI Monitoring Dashboard

Tier 1 MONITOR

ISO Clause 9 NIST MS-1

What This Requires

Create centralized dashboard monitoring key AI metrics: request volume, latency, error rate, model drift, bias indicators, cost per query, and SLA compliance. Update in real-time and review weekly.

Why It Matters

Visibility is the foundation of operational excellence. Dashboards enable rapid issue detection and data-driven decisions.

How To Implement

Select Metrics

Core metrics: request volume (total, per endpoint), latency (p50, p99), error rate (%), model drift score, bias fairness metrics, cost per 1K queries, SLA uptime. Tailor to system.

Choose Tooling

Use observability platform (Grafana, Datadog, New Relic). Integrate with logging (CloudWatch, Stackdriver) and tracing (Jaeger, Zipkin).

Build Dashboard

Create dashboard with panels per metric. Use time series charts (last 24h, 7d). Add annotations for deployments. Set auto-refresh (1 min).

Review Cadence

Weekly review with team: identify trends, anomalies, action items. Quarterly review SLA compliance and adjust thresholds.

Evidence & Audit

Dashboard URL or screenshots showing all required metrics
Metric definitions and calculation methodology
Integration configuration (logs, traces, cost APIs)
Weekly review meeting notes
Quarterly SLA compliance reports

Related Controls

Model Drift Detection AI Decision Audit Logs SLA & Performance Baselines