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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 the Windows Registry with Datadog
Nicholas Thomson, Shanel Huang · 2024-02-23 · via Datadog | The Monitor blog

The Windows Registry is a centralized key-value database that stores permissions, user data, and configuration settings for the Windows operating system and many Windows native applications. The keys stored in the registry provide a granular view into the processes occurring on a Windows host, such as certificate expirations, security checks, and pending reboots.

Datadog’s Windows Registry integration enables joint Windows and Datadog users to collect the value of any registry key in Datadog. This means Windows customers can now monitor unexpected changes of values only available in the registry. Monitoring these events can help teams stay ahead of issues caused by outdated registry values, ensure that permissions are in accordance with security best practices, and get detailed information on user behavior. This unified source of information can help teams to maintain the overall health of their Windows systems.

In this post, we’ll show you how to:

  • Understand how registry events impact performance

  • Create a Datadog Monitor to be notified when a critical registry key value changes

  • Create Cloud SIEM security signals and automated workflows from Windows Registry logs

Understand how registry events impact performance

The Windows Registry integration is included in the Datadog Agent package, so no installation is needed. You can use Log Management’s out-of-the-box pipeline to automatically parse, enrich, and tag your Windows events. Then, logs from Windows Registry will appear in the Log Explorer, where you can filter by the facet source:windows_ registry to see only Windows Registry logs, or get further granularity by filtering for the registry key or the message of the log you’re interested in.

View Windows Registry logs in the Datadog Log Explorer

You can use these logs to gain deep visibility into critical activity on your Windows hosts. For example, say you’re a security engineer, and you want to ensure that your application is compliant with security best practices. One potential vulnerability you are looking for is machines that have automatic logon enabled, as this can bypass security measures such as passwords.

You navigate to the Log Explorer and filter for Windows Registry logs that contain logon activity. You find a log from Windows Registry with the subkey: HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon and with a value that has just changed from 0 to 1, indicating that this machine has recently been switched to automatic logon.

With this knowledge in hand, you can investigate the machine further to determine if this is a possible sign of attacker activity, or if there is a valid reason for automatic logon being switched on.

Create a Datadog Monitor to be notified when a critical registry key value changes

The Windows Registry is the most granular source of information on a Windows machine, containing all information on users, their specific permissions, and other important data. This means you can use the registry to monitor a number of important changes—but the registry doesn’t allow you to create alerts natively. Instead of manually searching the registry for events pertinent to your incident investigations, you can stream the logs into Datadog, create custom metrics from them, and then create monitors on metric thresholds to alert you when a change occurs that you need to know about.

Say you’re an SRE at a Windows-native application, and you want to ensure that your app doesn’t lose functionality due to an expired certificate. So you decide to create a monitor on your system’s SSL certificate to alert you when it is near expiration.

Create Datadog monitors on Windows Registry events

To do this, you can create an alert that scans your logs tagged source:windows_registry looking for an indication of a certificate expiration warning in the messages. This will allow you to stay ahead of certificate expirations and ensure availability of your application.

Create Cloud SIEM security signals and automated workflows from Windows Registry logs

Windows Registry logs can be consumed by Datadog’s Cloud SIEM, which provides out-of-the-box detection rules for Windows, so you can monitor your systems for suspicious activity. Cloud SIEM can ingest, scan, and enrich any logs you send in from the Windows Registry, enabling you to add additional context to your detection rules.

For example, say you’re a security engineer, and you want to create a security signal from a registry log alerting you when Windows Defender is disabled, so you create a detection rule using this Windows Registry key: HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows Defender\Real-Time Protection\DisableRealtimeMonitoring.

Create detection rules from relevant Windows Registry keys

You can then create a workflow that automatically determines if the activity came from a suspicious IP and alerts the necessary members of your team if the threat is deemed serious.

Start monitoring Windows Registry activity

Datadog’s Windows Registry integration enables you to monitor, alert on, and enrich Cloud SIEM detection rules with data from the registry. This will help you troubleshoot issues affecting end users, enhance security guardrails to prevent malicious actors from taking advantage of vulnerabilities, and more.

If you’re new to Datadog, sign up for a 14-day free trial.