dd-audit-ai-activity
Audit what the Bits AI assistant (MCP server) has done in your Datadog org — tool calls by user, resources accessed, and anomaly flags for AI governance.
- Installs
- 435
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/datadog-labs/agent-skills --skill dd-audit-ai-activity- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads dd-audit-ai-activity automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
| 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.
References
FAQ
What does the dd-audit-ai-activity skill do?
Audit what the Bits AI assistant (MCP server) has done in your Datadog org — tool calls by user, resources accessed, and anomaly flags for AI governance.
How do I install dd-audit-ai-activity?
Run npx skills add https://github.com/datadog-labs/agent-skills --skill dd-audit-ai-activity in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/dd-audit-ai-activity/ (personal) or .claude/skills/dd-audit-ai-activity/ (project).
Which AI agents can use dd-audit-ai-activity?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is dd-audit-ai-activity safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains dd-audit-ai-activity?
It is published by datadog-labs in datadog-labs/agent-skills, and it is an official skill from the maker of the technology it covers. It has 435 recorded installs.
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