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 scopeQueries
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
| Signal | Governance concern |
|---|---|
AI performing deleted actions on monitors or dashboards |
Review whether destructive AI operations are expected |
AI acting as SUPPORT_USER |
Datadog support using AI on behalf of org |
| First-time user invoking AI tools | New user accessing AI assistant |
| High volume of tool calls in short window | Automated/batch AI usage |
| AI accessing resources outside user's normal scope | Potential over-permissioned AI session |
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.