/ai-activity-audit

Audit Trail: AI Activity Audit

Every Datadog MCP tool call is recorded in Audit Trail under the Bits AI SRE category. This skill surfaces what the AI assistant has done in your org — which users invoked it, which tools were called, and which resources were affected.

Prerequisites

pup auth login   # OAuth2 (recommended)
# or set DD_API_KEY + DD_APP_KEY with audit_logs_read scope

Queries

All MCP tool activity in a time window

pup audit-logs search --query "@evt.name:\"MCP Server\"" --from 7d --limit 500 -o json \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      user: .attributes.attributes.usr.email,
      actor_type: .attributes.attributes.evt.actor.type,
      action: .attributes.attributes.action,
      resource_type: .attributes.attributes.asset.type,
      resource_id: .attributes.attributes.asset.id,
      ip: .attributes.attributes.network.client.ip,
      country: .attributes.attributes.network.client.geoip.country.name
    }]'

Activity by user (who is using the AI assistant most?)

pup audit-logs search --query "@evt.name:\"MCP Server\"" --from 30d --limit 1000 -o json \
  | jq '[.data[] | .attributes.attributes.usr.email]
    | group_by(.)
    | map({user: .[0], tool_calls: length})
    | sort_by(-.tool_calls)'

Resources modified by AI tool calls

pup audit-logs search \
  --query "@evt.name:\"MCP Server\" @action:(created OR modified OR deleted)" \
  --from 7d --limit 500 -o json \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      user: .attributes.attributes.usr.email,
      action: .attributes.attributes.action,
      resource_type: .attributes.attributes.asset.type,
      resource_id: .attributes.attributes.asset.id
    }]'

AI activity for a specific user

pup audit-logs search \
  --query "@evt.name:\"MCP Server\" @usr.email:user@example.com" \
  --from 30d --limit 500 -o json \
  | jq '[.data[] | {
      timestamp: .attributes.timestamp,
      action: .attributes.attributes.action,
      resource_type: .attributes.attributes.asset.type,
      resource_id: .attributes.attributes.asset.id
    }]'

Weekly summary report

pup audit-logs search --query "@evt.name:\"MCP Server\"" --from 7d --limit 1000 -o json \
  | jq '{
      total_tool_calls: (.data | length),
      unique_users: ([.data[] | .attributes.attributes.usr.email] | unique | length),
      top_users: (
        [.data[] | .attributes.attributes.usr.email]
        | group_by(.)
        | map({user: .[0], calls: length})
        | sort_by(-.calls)
        | .[:5]
      ),
      actions_breakdown: (
        [.data[] | .attributes.attributes.action]
        | group_by(.)
        | map({action: .[0], count: length})
        | sort_by(-.count)
      ),
      resource_types: (
        [.data[] | .attributes.attributes.asset.type]
        | group_by(.)
        | map({type: .[0], count: length})
        | sort_by(-.count)
      )
    }'

Anomaly Flags

Output Format

AI Activity Audit — [Org] — [Date Range]

Total MCP tool calls: [N]
Unique users: [N]

Top users:
  [user@example.com]: [N] calls

Actions breakdown:
  accessed: [N]
  modified: [N]
  created: [N]
  deleted: [N]

Resource types affected:
  dashboard: [N]
  monitor: [N]

Anomalies:
  [List any flagged events with timestamp, user, action, resource]

Context

This skill is most useful for:

• Security reviews: Verifying AI actions were authorized and within expected scope
• Compliance audits: Demonstrating AI activity is logged and attributable to specific users
• Governance reports: Understanding adoption and risk surface of the AI assistant across the org

No other observability vendor audits their AI assistant's actions at this level of detail.

References

• Bits AI SRE documentation
• Audit Trail events — Bits AI SRE category
• MCP Server setup