Dd Audit Cost Spike Investigation

pup auth login   # OAuth2 (recommended) — covers audit queries
# Usage Metering queries also need DD_API_KEY + DD_APP_KEY
export DD_API_KEY=<your-api-key>
export DD_APP_KEY=<your-app-key>
export DD_SITE=datadoghq.com

START=$(date -u -v-7d +"%Y-%m-%dT%H:%M:%SZ" 2>/dev/null || date -u -d "7 days ago" +"%Y-%m-%dT%H:%M:%SZ")
END=$(date -u +"%Y-%m-%dT%H:%M:%SZ")

curl -s -G "https://api.${DD_SITE}/api/v2/usage/hourly_usage" \
  -H "DD-API-KEY: ${DD_API_KEY}" \
  -H "DD-APPLICATION-KEY: ${DD_APP_KEY}" \
  --data-urlencode "filter[timestamp][start]=${START}" \
  --data-urlencode "filter[timestamp][end]=${END}" \
  --data-urlencode "filter[product_families]=all" \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      product: .attributes.product_family,
      measurements: [.attributes.measurements[] | {type: .usage_type, value: .value}]
    }]'

pup audit-logs search \
  --query "@action:(created OR modified OR deleted)" \
  --from "SPIKE_TIME_MINUS_24H" \
  --to "SPIKE_TIME" \
  --limit 200 \
  -o json \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      user: .attributes.attributes.usr.email,
      actor_type: .attributes.attributes.evt.actor.type,
      action: .attributes.attributes.action,
      event_category: .attributes.attributes.evt.name,
      resource_type: .attributes.attributes.asset.type,
      resource_id: .attributes.attributes.asset.id
    }]'

pup audit-logs search \
  --query "@evt.name:(Integration OR APM OR \"Log Management\") @action:(created OR modified)" \
  --from "SPIKE_TIME_MINUS_24H" \
  --to "SPIKE_TIME" \
  --limit 100 \
  -o json \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      user: .attributes.attributes.usr.email,
      action: .attributes.attributes.action,
      category: .attributes.attributes.evt.name,
      resource_type: .attributes.attributes.asset.type,
      resource_id: .attributes.attributes.asset.id
    }]'

Usage spike detected:
  Product: <product_family>
  Spike time: <SPIKE_TIME>
  Volume: <baseline> → <spike_value> (<magnitude>×)

Configuration changes in 24h preceding spike:
  <timestamp> | <user_email> | <action> <resource_type> <resource_id> | <category>

Likely causal change: <most-proximate change matching the product family>

Confidence: HIGH (single clear change) / MEDIUM (multiple candidates) / LOW (no matching changes)

Next steps:
  - Confirm with <user_email> whether the change was intentional
  - If unintentional: revert <resource_id> and monitor volume
  - If intentional: update cost forecasts and alert thresholds