dd-audit

Prüfpfad-Untersuchungen – wer was geändert hat, Schlüsselkompromittierung, Ursache von Kostenanstiegen, Compliance-Nachweise (SOC 2/PCI) und Prüfung von KI-Aktivitäten.

npx skills add https://github.com/datadog-labs/agent-skills --skill dd-audit

Datadog Audit Trail

Investigate user activity, configuration changes, access patterns, and compliance evidence using pup audit-logs.

Sub-Skills

Sub-skillUse when
security-investigation"Who changed X?", "What did this user do?", "Show me deletions in the last 24h"
key-compromise"Was this API key compromised?", "What did key XYZ do?", "Investigate suspicious key activity"
cost-spike-investigation"Why did my bill go up?", "What caused this usage spike?", "Investigate LLM cost increase"
compliance-report"Generate SOC 2 evidence", "PCI audit log", "User provisioning report for auditor"
ai-activity-audit"What did the AI assistant do?", "Audit MCP tool calls", "AI governance report"

Prerequisites

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

Commands

# List recent events
pup audit-logs list --from 1h --limit 100

# Search with a query
pup audit-logs search --query "@action:deleted" --from 24h

# JSON output for piping to jq
pup audit-logs search --query "@usr.email:alice@example.com" --from 7d -o json | jq '.data[].attributes'

Event Schema Quick Reference

FieldDescriptionExample values
@usr.emailActor emailalice@example.com
@evt.actor.typeHow action was takenUSER, API_KEY, SUPPORT_USER
@actionVerbcreated, modified, deleted, accessed, login
@evt.nameEvent categoryDashboard, Monitor, Authentication, Access Management
@asset.typeResource typedashboard, monitor, api_key, role, user
@asset.idResource identifierabc-123
@metadata.api_key.idAPI key used (if applicable)key_abc123
@metadata.app_key.idApp key used (if applicable)app_abc123
@network.client.ipClient IP address1.2.3.4
@network.client.geoip.country.nameCountryUnited States
@network.client.geoip.as.nameASN nameAmazon.com
@http.url_details.pathAPI endpoint path/api/v1/dashboard/xyz

Search Syntax

Same Lucene-style syntax as Log Explorer:

QueryMeaning
@evt.name:DashboardExact field match
@action:deletedAction filter
@usr.email:alice@example.comSpecific user
@evt.name:Monitor AND @action:modifiedCompound
-@action:deletedNegation
@usr.email:*Field exists
@network.client.ip:1.2.3.4IP filter

Retention

Default retention is 90 days. If querying beyond 90 days, archive to S3/GCS/Azure Blob must be configured. Always check whether the requested time window falls within retention before running a query.

Troubleshooting

ProblemCauseFix
403 ForbiddenMissing audit_logs_read scopeAdd scope to app key in Datadog UI
Empty resultsTime window outside retentionCheck archive config; default max is 90 days
TimeoutQuery too broadNarrow time window or add more filters
No IP dataInternal action or pre-enrichment eventNot all events have geo data

References

Mehr Skills von datadog-labs

agent-install
datadog-labs
Installieren Sie den Datadog Agent auf Kubernetes mit dem Datadog Operator – erforderlich, bevor Single Step Instrumentation (SSI) aktiviert werden kann, das automatisch…
official
agent-observability-auto-experiment
datadog-labs
Führe einen iterativen Code-Verbesserungs-Hill-Climb gegen echte Datadog-LLM-Obs-Daten lokal mit Claude Code als Agent durch. Etabliert eine Baseline-Evaluierung, macht eine…
official
agent-observability-eval-bootstrap
datadog-labs
Bootstrappen Sie Evaluatoren aus Produktionstraces — standardmäßig werden Online-LLM-Judge-Evaluatoren vorgeschlagen, und nach Ihrer Bestätigung werden sie in Datadog als deaktivierte Entwürfe erstellt…
official
agent-observability-eval-pipeline
datadog-labs
End-to-End-Agent-Observability-Pipeline für eine instrumentierte ml_app — Produktions-Traces klassifizieren, Fehlerursachen ermitteln, Evaluatoren bootstrapen, dann (optional)…
official
agent-observability-experiment-analyzer
datadog-labs
Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare…
official
agent-observability-replay-trace
datadog-labs
Verwenden Sie dies, wenn ein Entwickler an EINER bestimmten Agent Observability / LLM Obs-Trace iterieren möchte, deren Ausgabe ihm nicht gefallen hat — indem er diese Trace erneut gegen seine…
official
agent-observability-trace-rca
datadog-labs
Ursachenanalyse bei Produktions-LLM-Traces. Diagnostiziert, warum eine LLM-Anwendung fehlschlägt – arbeitet mit Eval-Judge-Urteilen, Laufzeitfehlern oder strukturellen…
official
agent-skills
datadog-labs
Datadog skills for AI agents. Essential monitoring, logging, tracing and observability.
official