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Datadog | The Monitor blog

Introducing our open source AI-native SAST Instrument and monitor Boomi integration flows with OpenTelemetry and Datadog Not all index scans are equal: How we cut query latency by over 99% Platform engineering metrics: What to measure and what to ignore Integrate Recorded Future threat intelligence with Datadog Cloud SIEM CI/CD security: threat modeling using a MITRE-style threat matrix CI/CD security: How to secure your GitHub ecosystem Ingress NGINX is EOL: A practical guide for migrating to Kubernetes Gateway API Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA Introducing the Datadog Code Security MCP Capture and analyze custom heatmaps in Session Replay Understand session replays faster with AI summaries and smart chapters Monitor ClickHouse query performance with Datadog Database Monitoring How we designed empathetic alert sounds for on-call engineers Search and act across Datadog to resolve issues faster with Bits Assistant Measure the business impact of every product change with Datadog Experiments Analyzing round trip query latency Configuring JavaScript caches for better performance Introducing Bits AI Dev Agent for Code Security Datadog achieves ISO 42001 certification for responsible AI Monitor Nutanix clusters, hosts, and VMs with Datadog Monitor Juniper Mist in Datadog A new Host Map for modern infrastructure Annotate traces to improve LLM quality with Datadog LLM Observability What’s new in Cloud SIEM: AI-powered investigations, enhanced threat intelligence, and scalable security operations Explore Kubernetes with native OpenTelemetry data Monitor Oracle Fusion Cloud Applications with Datadog Announcing the Datadog Terraform provider v4.0.0 Scaling Kubernetes workloads on custom metrics How to design cloud environments for AI-powered threat analysis Monitor Aruba Central in Datadog How we centralize and remediate risks with Datadog Case Management Accelerate incident response with Datadog and ServiceNow Monitor your application and network load balancer logs Understanding Karpenter architecture for Kubernetes autoscaling Tools for collecting metrics and logs from Karpenter Monitor Karpenter with Datadog What your product data is actually saying Key metrics for monitoring Karpenter Securing Datadog’s platform in the AI age: The role of observability data Four ways engineering teams use the Datadog MCP Server to power AI agents Approaching your observability migration with the right mindset Meet the new Bits AI SRE: Deeper reasoning, twice as fast Key learnings from the 2026 State of DevSecOps study Use plain English to query your multi-cloud infrastructure in Resource Catalog Simplifying troubleshooting across the user journey with Datadog Synthetic Monitoring Protect your OCI resources with Datadog Cloud Security This Month in Datadog - February 2026 Amazon EC2 security: How misconfigured and public AMIs expand your cloud attack surface Enable end-to-end visibility into your Java apps with a single command Measure and improve mobile app startup performance with Datadog RUM Evaluating our AI Guard application to improve quality and control cost Identify untested code across every level of your codebase Make use of guardrail metrics and stop babysitting your releases Monitor Versa Networks SD-WAN performance in Datadog Improve performance and reliability with APM Recommendations Remediate transitive vulnerabilities faster with Datadog Software Composition Analysis Generate audit-ready vulnerability and compliance reports with Datadog Sheets Monitor Fortinet FortiManager performance in Datadog Improve test coverage across codebases with Datadog Code Coverage Move fast, don’t break things: Consistent testing standards at scale Enrich logs with ServiceNow CMDB context before routing to any SIEM or logging tool Monitor Lustre with Datadog Make faster, better product decisions with Datadog Product Analytics Surface and remediate runtime posture issues with Workload Protection Findings Protect agentic AI applications with Datadog AI Guard How to optimize JavaScript code with CSS Trace Google Pub/Sub workloads in Cloud Run with Datadog Detect human names in logs with ML in Sensitive Data Scanner How we cut our NLQ agent debugging time from hours to minutes with LLM Observability Debug PostgreSQL query latency faster with EXPLAIN ANALYZE in Datadog Database Monitoring Datadog acquires Propolis Unify and correlate frontend and backend data with retention filters Scale compliance across global frameworks with Datadog Cloud Security Monitor Arista VeloCloud SD-WAN performance with Datadog Building reliable dashboard agents with Datadog LLM Observability Simplify log collection and aggregation for MSSPs with Datadog Observability Pipelines Mitigation for Node.js denial-of-service vulnerability affecting Datadog APM Automate flaky test fixes with the Bits AI Dev Agent and Test Optimization How we built an AI SRE agent that investigates like a team of engineers Datadog integrations 2025 recap: Observability for AI, security, and hybrid cloud Design effective executive dashboards with Datadog Implement dbt data quality checks with dbt-expectations Bring faster visibility into AWS Lambda functions with remote instrumentation Troubleshoot faster with the GitLab Source Code integration in Datadog How Cambia Health Solutions saved $30,000 monthly with Cloud Cost Management and the Datadog Resource Catalog Normalize any logs for Cloud SIEM with Datadog's OCSF processor Optimizing Datadog at scale: Cost-efficient observability at Zendesk Detect, diagnose, and resolve network issues easily with CNM Network Health Connect engineering errors to user impact in early-stage products Cilium configuration for Kubernetes operations at scale Designing feedback loops for progressive delivery Ship features faster and safer with Datadog Feature Flags Choosing the right OpenTelemetry Collector distribution Route your monitor alerts with Datadog monitor notification rules Automate Cloud SIEM investigations with Bits AI Security Analyst Cloud threat detection: How to identify risky activity across control and data planes Collecting Kafka performance metrics Monitoring Kafka with Datadog Monitoring Kafka performance metrics
Monitor critical Datadog assets and configurations with Audit Trail
Jordan Obey, Anshum Garg · 2022-09-20 · via Datadog | The Monitor blog

Datadog Audit Trail provides administrators and security team members visibility into how different users and teams within their organizations use and interact with various Datadog products and observability data. If you are running a service at scale—with dozens of teams and users sharing a growing number of dashboards, pipelines, and alerts—but only a small team of Datadog administrators are dedicated to keeping track of usage patterns and accessibility, it can be difficult to identify how each team’s activity impacts the work of another. With Audit Trail, you can get a centralized view of activity in Datadog for valuable usage insights. For example, if a critical dashboard suddenly breaks after a configuration change, Audit Trail enables you to see which user or team made the change so that you can better determine what happened, which allows you to establish a robust Datadog monitoring setup.

In this post, we’ll look at how teams in your organization can fully leverage Audit Trail to:

  • Track configuration changes to maintain a robust monitoring setup

  • Monitor Datadog activity for security and usage insights

Track configuration changes

One of the top priorities of Datadog administrators is to ensure that their organizations have a robust monitoring setup, and that developer operational workflows remain uninterrupted—which becomes more challenging as organizations grow. For instance, in a large ecommerce service with thousands of users and hundreds of teams sharing the same dashboards, data sources, and monitors, it can be difficult to determine how changes one team makes can impact the organizational workflow of another.

With Audit Trail, you can investigate unexpected issues by digging into audit events to reveal when segments of the Datadog platform were accessed or modified and by whom. Audit events are particularly useful for uncovering product-specific actions such as:

  • dashboard updates

  • log management configuration changes

  • changes to custom metrics and tags

Dashboard updates

DevOps teams commonly rely on dashboards to monitor the health and performance of critical services. Therefore, any changes to a dashboard, such as the addition or removal of a query or function from a graph, may have unintended consequences and result in decreased visibility into critical troubleshooting telemetry.

If someone makes a breaking change to a critical dashboard, you can navigate to the Audit Trail page (which can be found under “Organization Settings”) and use the search terms Event Name:Dashboard and Action:modified to see recent dashboard changes and the users responsible so that you can follow up accordingly.

audit-trail-01

Log management configuration changes

Security teams often use Datadog’s log processing pipelines to parse logs and enrich them with contextual metadata—such as team tags, geography information, IP information, and other infrastructure-related information—so they can better pinpoint threats and set up alerts. To ensure that your logs are properly enriched and identify downstream alerts that may be affected, you can use the “Changes made to Pipelines” and “Changes made to Pipeline Processors” tables in the Audit Trail overview dashboard. These tables enable you to quickly see whether pipelines and processors were changed and when those changes took place. For example, an engineer could have accidentally misconfigured grok rules for parsing custom application logs. If you see that an unplanned change was made to a pipeline or process, you can pivot to the Audit Trail page to explore the event in greater detail to see what changes occurred.

audit-trail-02

Changes to custom metrics and tags

Organizations usually monitor business-specific data by using custom metrics with tags on customer IDs, locations, and item types. These tags enable teams to slice and dice metrics so they can quickly access the data they need. Changing or removing custom metrics and tags can lead to disrupted analyst workflows, broken executive dashboards, and other unintended consequences. You can search for audit events that capture changes made to a metric if associated tags that teams rely on have been changed. In the example below, by viewing recent changes to the shopist.basket.size metric, we can see that it was deleted by a user. You can then follow up with that user to determine whether that deletion was a mistake.

audit-trail-03

Monitor Datadog activity for security and usage insights

Another top priority for administrators is ensuring that Datadog is being used securely. To that end, admins need visibility into who has access to their organization’s Datadog account. Audit Trail allows you to track the creation, deletion, and modification of Datadog API and APP keys. This capability allows you to set an alert to notify you if the number of API deletions exceeds a threshold in a short time frame, which may indicate a disruption in your monitoring setup.

audit-trail-04

In addition to ensuring the security of your Datadog account, insight into current usage can help drive wider and more effective adoption of monitoring tools. For instance, you can configure the Audit Trail page to show the number of unique users for each Datadog feature. This knowledge can help you determine where to focus an enablement plan that encourages teams to increase adoption of specific features for their use cases. For example, if you see that Datadog Application Performance Monitoring (APM) is leveraged by users less than expected, it may mean that teams within your organization need more guidance on how APM can help them meet specific goals.

audit-trail-05

You should also consider setting alerts on specific audit events that can impact your monitoring, such as changes to your log retention settings and the enablement of any new features. For instance, if your log retention settings are changed so that you retain more logs than necessary, it can make it harder to find relevant troubleshooting data because you will have a greater volume of logs to analyze.

Enhance your Datadog experience by using Audit Trail today

In this post, we looked at how you can use Audit Trail to see how Datadog is used across your organization. This lets you keep track of configuration changes involving dashboards, monitors, and custom metrics, as well as optimize the security and efficiency of your monitoring efforts by notifying on API key changes or unused Datadog products and features. You can learn more about how Audit Trail provides you with insight into your organization’s Datadog usage by checking out our documentation.

If you’re not already a Datadog customer, sign up today for a 14-day free trial.