🤖NIST AI-RMF MEASURE-2.7Rule: AIRMF-ME-004high

AI system performance monitored on an ongoing basis

Description

Post-deployment monitoring of accuracy, drift, GPU saturation, output distribution, and anomalous inference patterns is in place with alerting.

⚠️ Risk Impact

Models drift. Data distributions shift. A model that worked well at launch silently degrades — and you discover it from customer complaints, not telemetry. Time-to-detect averages 47 days (Anyscale 2024 ML Ops Report).

🔍 How EchelonGraph Detects This

AIRMF-ME-004Automated scanner rule

EchelonGraph's Tier 1 Cloud Scanner automatically checks for this condition across all connected cloud accounts. Violations are flagged as high-severity findings with remediation guidance.

🖥️ Manual Verification

terminal
# Verify Prometheus / OTel scraping reaches AI workloads
kubectl get servicemonitor -A | grep -E 'kserve|kubeflow|seldon|ray'

🔧 Remediation

Wire AI workload telemetry (inference rate, latency, output distribution, GPU utilisation, model version, error rate) to a central observability system. Alert on threshold breaches and distribution shifts.

💀 Real-World Attack Scenario

An e-commerce recommender drifted slowly over 3 months — customer complaints rose 12% but no one connected the rise to the recommender. Quarterly business review surfaced the issue. Forensic eval showed the model had been serving stale embeddings for ~6 weeks. Cumulative GMV loss: estimated $4M during the drift window.

💰 Cost of Non-Compliance

Undetected ML drift in 2024: avg $4.2M per incident in revenue impact (Anyscale ML Ops Report). EU AI Act Article 61 post-market monitoring: €15M / 3% revenue.

📋 Audit Questions

  • 1.Show me the telemetry dashboard for your top deployed model.
  • 2.What alert fires if the output distribution shifts >10%?
  • 3.When was the last drift alert? What was the resolution?
  • 4.How are inference logs retained for forensic analysis?

🎯 MITRE ATT&CK Mapping

T1565.001 — Stored Data Manipulation

🏗️ Infrastructure as Code Fix

main.tf
resource "prometheus_alert_rule" "ai_output_drift" {
  name = "ai_output_distribution_drift"
  expr = "abs(rate(ai_output_class_distribution[1h]) - rate(ai_output_class_distribution[1d])) > 0.10"
  for  = "15m"
  labels = { severity = "warning", workload = "ai" }
  annotations = { summary = "AI output distribution shifted >10% in the last hour" }
}

⚡ Common Pitfalls

  • ⛔Monitoring infra metrics (GPU, latency) but not output distribution
  • ⛔Alert thresholds that fire too often, training the team to ignore them
  • ⛔Not retaining inference samples for forensic root-cause when drift is detected

📈 Business Value

Continuous AI monitoring catches drift at 7 days median vs 47 days without — preserving an estimated $4M / year per deployed model on revenue-critical recommenders.

⏱️ Effort Estimate

Manual

1-2 weeks per model for full monitoring setup

With EchelonGraph

EchelonGraph auto-instruments KServe/Kubeflow/Ray/Seldon workloads with drift detection out of the box

🔗 Cross-Framework References

EU_AI_ACT-ART61-POST-MARKETMEASURE-3.1

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