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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
Datadog achieves FedRAMP Moderate Impact authorization
2022-01-26 · via Datadog | The Monitor blog

As government agencies accelerate migrating their operations to the cloud, they need to adhere to strict compliance and security standards. The Federal Risk and Authorization Management Program (FedRAMP) provides the standard that these agencies—and their private-sector partners—must meet to work and manage federal data safely in the cloud. We’re excited to announce that Datadog has achieved FedRAMP Moderate Impact authorization, meaning that public-sector organizations can use Datadog to monitor the health, performance, and security of all of their Moderate Impact-level applications and infrastructure. As the need for modern, reliable public-sector web applications grows, agencies can expect greater user traffic across their websites and applications in the near future. Now, they can now use Datadog to help them securely meet their growing monitoring demands.

Unified visibility into the public sector’s cloud

Datadog integrates with major cloud providers like AWS, GCP, and Azure, meaning that agencies that rely on collaborating across shared, distributed infrastructures can get full visibility into their Moderate Impact–level systems running in the cloud. Monitoring such systems with disparate tools can be impossibly complex. Datadog provides a unified solution that lets you monitor the health and performance of your infrastructure, applications, networks, users’ experiences, and more across your cloud environment, all from a single platform.

You can get a full-picture perspective of the health of your AWS GovCloud (US) infrastructure from an all-in-one dashboard.

For instance, DevOps teams at a scientific agency may be concerned with the rate at which researchers can store and transfer data from their cloud-hosted databases. Now, they can use Datadog to set alerts on key metrics to get notified whenever database throughput reaches a throttling limit. This way, they can stay on top of demand across their organization and quickly provision more databases or load balancers to decrease service downtime.

Datadog Synthetic Monitoring lets you simulate user behavior without the risk of live service outages.

As another example, government web portals receive traffic from users across the country who want to enroll in or check the status of their public-sector services. To test the availability of their infrastructure, agencies can use Datadog’s Synthetic Monitoring to simulate user requests and identify potential weak spots without the risk of a live service outage.

With the host map, agencies can visualize the status of each server across their infrastructure and get a high-level overview of key metrics like CPU utilization and network throughput.

Host maps visually organize your assets based on their performance, making it easy to spot and address anomalies.

This makes it easy for teams to spot anomalies in their hosts’ performance, then dive deep into their particulars and address their root causes. Then, using Datadog’s cross-stack metric correlation tools, they can quickly detect adjacent assets in their stack that may be contributing to the anomaly, speeding up troubleshooting and helping ensure that their services remain healthy.

Datadog, reporting for duty

With FedRAMP Moderate Impact authorization, Datadog is ready to help you manage your public cloud-monitoring needs. And by integrating with more than 1,000 technologies, Datadog gives you full visibility into your cloud infrastructure. If you’re already a Datadog customer, you can start setting up your AWS GovCloud (US) monitoring tools now. And if you’re not already using Datadog, get started now with a 14-day free trial.