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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
Build compliance, governance, and transparency across your teams with Datadog Audit Trail
Addie Beach, Anshum Garg · 2022-06-09 · via Datadog | The Monitor blog

In order to maintain compliance, enforce governance, and build transparency, teams across your organization need deep insight into how their users and automation interact with Datadog. For stakeholders in leadership roles, such as CIOs and CDOs, knowing what actions users took and when is essential for spotting gaps in enablement, budgeting, and reporting, as well as building a modern compliance strategy for the organization as a whole. At the same time, administrators and security analysts need visibility to ensure that operational workflows stay intact and to follow up when incidents occur.

With Datadog Audit Trail, you have access to granular, centralized records of user and API activity throughout the platform. You can easily analyze these records in our out-of-the-box dashboard, as well as alert on key system events that have billing and access impact. This information allows your organization to keep up with compliance checks, ensure best practices, and share insights with stakeholders.

Top list of recently modified Datadog assets.

Build a modern compliance strategy with Datadog

Datadog Audit Trail provides end-to-end visibility into user actions, so you can see the path each user took in the app, and then use that information to diagnose issues and implement measures to keep your systems compliant.

You can leverage Audit Trail to help your organization meet crucial privacy and reporting standards like the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), Federal Risk and Authorization Management Program (FedRAMP), and California Consumer Privacy Act (CCPA). The information you obtain from Audit Trail can assist you with customizing log index retention times elsewhere in the system, and you can view Audit Trail events for up to 90 days in Datadog with archiving options for long-term storage. Additionally, you can use Audit Trail alongside Datadog Cloud Security Information and Event Management (SIEM) to unify security tasks across your organization.

Audit Trail comes with an out-of-the-box dashboard that allows you to visualize day-to-day user activity. Detailed graphics help you to spot unusual behavior, such as spikes in authentication events that could be caused by automated attacks. You can also set up alerts on system events to automatically notify you about anomalies or outliers, then dive into Audit Trail to investigate. In the case of a security breach, Audit Trail allows you to identify the malicious users, see what they may have accessed, and manage the blast radius.

Out-of-the-box dashboard for Datadog Audit Trail.

Building a modern compliance strategy requires that you both monitor and assess system activity. When used alongside Role Based Access Control (RBAC) and Sensitive Data Scanner (SDS), Audit Trail allows you to do just this. RBAC and Sensitive Data Scanner both help you proactively manage access in Datadog, and Audit Trail gives you the data you need to retrospectively investigate and diagnose security issues that can affect compliance.

A common scenario for organizations is to use RBAC to restrict access to dashboards that contain sensitive user information, such as usernames and passwords, and then use Sensitive Data Scanner to redact that information elsewhere in Datadog. For example, let’s say you notice that a recent change to a custom role allows certain users to see dashboards they shouldn’t have access to. After fixing the configuration, you can use Audit Trail to determine which Datadog users viewed the dashboards. You’re then able to confirm that only users with the proper authorization saw the exposed information.

Ensure Datadog platform governance across teams

Unintentional changes to Datadog resources—such as API keys, monitors, dashboards, and log pipelines—can disrupt your team’s workflows. Automated systems can magnify the impact of interruptions even more, potentially breaking CI/CD pipelines or tools like Terraform. These disruptions can be difficult to prevent, with blindspots in asset ownership or process design making it hard to catch problematic changes and figure out who to contact. And even after you fix any issues with your own workflow, there may be other system-wide impacts that go unnoticed.

With Datadog Audit Trail, you can easily view a list of recent actions performed in the system to help you investigate and diagnose issues caused by configuration changes. By narrowing the Audit Trail events to a certain timeframe, you can identify any unusual activity around the time the incident occurred. You can also use filters such as event ID and authentication method to quickly identify the root cause, as well as view associated user IDs and emails to determine who controls the resource in question.

Let’s say a monitor your team relies on for infrastructure health updates is no longer showing data from your hosts. By digging into the audit events, you notice that a member of a frontend dev team recently made changes to the monitor. You reach out to this user and determine that they accidentally modified it while configuring one for their own team. You’re then able to work together to undo the updates and create new best practices to prevent future misconfigurations.

Audit Trail also gives you visibility into Datadog configuration changes that may affect your budget, such as modifications to log index retention times and sampling of noisy log events. Log indexes can be useful for managing cost by allowing your organization to retain only the most essential data long-term. Using Audit Trail, you can set up monitors to notify you about any log index or exclusion filter changes. If a user adjusts either of these configurations, you can reach out to them to confirm whether it was a mistake.

An active alert for changes to a log index.

Increase transparency using context-rich insights

With Datadog Audit Trail, you can easily share findings and reports about your Datadog usage with other stakeholders, including audit, security, and Governance, Risk, and Compliance (GRC) teams. You can export views and events directly to monitors, incidents, notebooks, and dashboards to enrich your resources with added context. Additionally, you can also send these views directly to team members who may be involved in an audit or other investigation.

In the situation described above, you could share the modified indexes event with your organization’s billing team. They would then be able to investigate these changes to determine any impact to your organization’s cost and take remediatory steps, such as reducing the retention periods for other unnecessary data to compensate. You could also send the event details to your security and GRC teams to determine whether this user should’ve had the access needed to change these indexes in the first place. If not, you could add these findings to follow-up audit reports.

Export and download menu for a top list of recently modified asserts, including options for CSV, PNG, and SVG formats.

Start monitoring Datadog platform adoption with Audit Trail today

Datadog Audit Trail gives you full visibility into your Datadog platform adoption, enabling you to meet compliance, governance, and transparency standards across your organization. You can use Audit Trail alongside other Datadog services, such as Cloud SIEM, RBAC, and Sensitive Data Scanner, for greater visibility into and control over your teams’ Datadog usage. Additionally, Audit Trail can be used with or without Datadog Log Management.

If you’re an existing customer, you can get started with Datadog Audit Trail using our documentation. Or, if you’re new to Datadog, you can sign up for a 14-day free trial.