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Anomaly Detection & Guardrails

BEAK Governance uses machine learning to detect unusual cost patterns and enforces guardrails to prevent unexpected spend.

Anomaly Detection​

How It Works​

ML models learn your organization's normal spending patterns and alert when costs deviate significantly from expected baselines.

Detection Types​

  • Spike Detection — Sudden cost increases (e.g., runaway compute, accidental deployment)
  • Drift Detection — Gradual cost creep that may go unnoticed manually
  • Pattern Anomalies — Unusual spending patterns (weekends, off-hours, new services)

Alert Channels​

  • Email notifications to cost owners
  • Slack/Teams integration for real-time alerts
  • Dashboard indicators with drill-down capability

Guardrails​

Budget Guardrails​

  • Set hard/soft budget limits per account, team, or project
  • Automated actions when thresholds are breached (alert, throttle, restrict)
  • Approval workflows for spend above thresholds

Policy Guardrails​

  • Restrict instance types and sizes per environment
  • Enforce region restrictions for compliance
  • Block resource creation without required tags
  • Limit permissions for cost-impacting actions

Automated Response​

  • Scale down non-production environments outside business hours
  • Terminate idle development resources after configurable timeout
  • Pause non-critical workloads when budget thresholds are reached