惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

酷 壳 – CoolShell
酷 壳 – CoolShell
H
Hacker News: Front Page
P
Palo Alto Networks Blog
T
ThreatConnect
Apple Machine Learning Research
Apple Machine Learning Research
博客园_首页
T
True Tiger Recordings
P
Privacy & Cybersecurity Law Blog
B
Blog
IT之家
IT之家
Last Week in AI
Last Week in AI
F
Full Disclosure
Hacker News: Ask HN
Hacker News: Ask HN
C
Comments on: Blog
Microsoft Azure Blog
Microsoft Azure Blog
C
Cybersecurity and Infrastructure Security Agency CISA
Microsoft Security Blog
Microsoft Security Blog
博客园 - 【当耐特】
N
News and Events Feed by Topic
NISL@THU
NISL@THU
腾讯CDC
雷峰网
雷峰网
Security Latest
Security Latest
李成银的技术随笔
M
Microsoft Research Blog - Microsoft Research
L
LangChain Blog
L
Lohrmann on Cybersecurity
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
C
Check Point Blog
Y
Y Combinator Blog
Recent Announcements
Recent Announcements
博客园 - Franky
N
News | PayPal Newsroom
V
V2EX
A
About on SuperTechFans
The Register - Security
The Register - Security
月光博客
月光博客
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Google Online Security Blog
Google Online Security Blog
MyScale Blog
MyScale Blog
Cisco Talos Blog
Cisco Talos Blog
Vercel News
Vercel News
WordPress大学
WordPress大学
C
Cyber Attacks, Cyber Crime and Cyber Security
The Hacker News
The Hacker News
IntelliJ IDEA : IntelliJ IDEA – the Leading IDE for Professional Development in Java and Kotlin | The JetBrains Blog
IntelliJ IDEA : IntelliJ IDEA – the Leading IDE for Professional Development in Java and Kotlin | The JetBrains Blog
爱范儿
爱范儿
A
Arctic Wolf
L
LINUX DO - 最新话题
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

Datadog | The Monitor blog

Reduce CVE noise with OpenVEX assessments in Datadog How we made a SQL query optimization agent 59% more accurate using autoresearch and LLM Observability How to audit and clean up monitors effectively Diagnose slow PostgreSQL queries faster with explain plan correlation Explore Datadog metrics with Natural Language Queries Toto 2.0: Time series forecasting enters the scaling era Simplify micro-frontend observability with Datadog RUM Attribute AI costs across providers with Datadog Cloud Cost Management Diagnose and resolve database performance issues faster with Database Investigator Datadog for Government achieves FedRAMP® High certification Analyze cloud costs with flexible spreadsheets in Datadog Sheets Inside Datadog’s AI Research Lab: Meet two PhD candidates behind Toto Connect triage and investigation in a single workflow with Datadog Cloud SIEM This Month in Datadog - April 2026 Monitor and optimize Supabase query performance with Datadog Database Monitoring Add dynamically updating context to logs with Reference Tables and Observability Pipelines Introducing ARFBench: A time series question-answering benchmark based on real incidents The product signal latency gap slowing your growth Test network paths with TCP, UDP, and ICMP in Datadog Turn developer feedback into operational insight with Datadog Forms and Sheets How to investigate cloud credential compromise with Bits AI Security Analyst Evaluate, optimize, and secure your Google Cloud AI stack with Datadog Bringing observability data hosting to the UK on AWS Identify and fix code issues faster with Datadog’s Azure DevOps Source Code integration Steganography at scale: Embedding share URLs in Datadog widget screenshots Every team should be A/B testing Centralize observability management with Datadog Governance Console Spotting CI/CD misconfigurations before the bots do: Securing GitHub Actions with Datadog IaC Security Route OTel data from AI apps to ClickHouse and Datadog using Observability Pipelines Manage service tracing across hosts with Single Step Instrumentation rules Offline evaluation for AI agents: Best practices Detect runtime threats in Python Lambda functions with Datadog AAP 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 How we built a real-world evaluation platform for autonomous SRE agents at scale 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 When upserts don't update but still write: Debugging Postgres performance at scale 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 Closing the verification loop: Observability-driven harnesses for building with agents When an AI agent came knocking: Catching malicious contributions in Datadog’s open source repos Closing the verification loop, Part 2: Fully autonomous optimization 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 Designing MCP tools for agents: Lessons from building Datadog's MCP server 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 Fine-tune Toto for turbocharged forecasts 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 How we reduced the size of our Agent Go binaries by up to 77% 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
Why FedRAMP® High Observability Matters for Government IT Teams
2025-05-09 · via Datadog | The Monitor blog
Greg Reeder

Greg Reeder

Geoffrey Carlisle

Geoffrey Carlisle

As government agencies modernize infrastructure and migrate sensitive workloads to the cloud, the stakes for maintaining strict compliance and security standards are higher than ever. Building on our commitment to the US public sector, Datadog is proud to announce that we’ve achieved “In Process” status for FedRAMP® High authorization. This is another milestone on our path to providing the highest level of cloud security and observability for government agencies.

This designation allows government IT leaders and engineers to prepare for full-stack observability at the highest federal compliance level. In this post, we’ll explain what FedRAMP High means, why observability is essential for secure modernization, and how Datadog supports this mission.

What is FedRAMP High?

The Federal Risk and Authorization Management Program (FedRAMP) defines FedRAMP High as the most rigorous security baseline. It applies to cloud systems that store or process data where a breach could have catastrophic effects on operations or individuals. These systems often include:

  • Financial data and taxpayer services
  • Protected Health Information (PHI)
  • Law enforcement, public safety, and homeland security systems
  • Controlled Unclassified Information (CUI)
  • Emergency services and disaster response platforms

Cloud platforms supporting these use cases must meet NIST 800-53 Rev. 5 High Baseline controls, demonstrating a strong commitment to secure cloud operations.

Why Having FedRAMP High authorization matters for observability

Compliance is essential—but it’s just the starting point. Government IT teams also need real-time insight across their hybrid and multi-cloud systems to detect anomalies, resolve issues, and meet evolving mandates like OMB M-21-31, EO 14144, and TIC 3.0.

With a FedRAMP High–authorized observability solution, agencies can:

  • Prevent downtime and disruptions to mission-critical services
  • Gain actionable insights into infrastructure and application performance
  • Proactively monitor for misconfigurations or suspicious behavior
  • Support Zero Trust principles and incident response workflows
  • Optimize operations for speed, security, and scale

Unified monitoring for secure government workloads

Government agencies face persistent challenges with tool sprawl, siloed data, and limited visibility across complex environments. The need for efficiency and optimization drives the increasing requirement for integrated solutions that provide comprehensive visibility into complex environments.

Datadog addresses these challenges with a single, unified platform that provides end-to-end visibility into the health, performance, and security of your complex environments. With Datadog, teams can:

  • Monitor performance metrics across their entire infrastructure and network, from on-premises servers and devices to cloud services, Kubernetes clusters, and more than 800 other technologies
  • Collect distributed traces to measure application performance and identify bottlenecks before they cause customer impact
  • Identify and triage security threats and vulnerabilities across their infrastructure and applications
  • Ingest, process, and store logs for deep analysis and adherence to federal logging mandates

Additionally, Datadog’s AI-powered anomaly detection engine, Watchdog, automatically and continuously monitors your environment to surface and alert you to issues before they impact users.

What can you achieve with FedRAMP High-authorized observability from Datadog?

FedRAMP High-authorized observability with Datadog supports ongoing efforts to modernize federal IT operations by providing agencies with a secure, unified platform that delivers more than just compliance—it powers better decision-making, operational agility, and measurable cost savings.

Here’s how Datadog helps government succeed, securely, efficiently, and at scale:

Gain full visibility

Datadog provides full visibility into system health across hybrid, multi-cloud, and containerized environments. Teams can monitor Kubernetes clusters, VMs, and serverless workloads—all in one place.

Build unified dashboards to monitor system health across complex environments.
Datadog dashboard showing system health across multi-cloud environment
Build unified dashboards to monitor system health across complex environments.

For example, if a DevOps team at a federal benefits agency is rolling out AI-powered digital services across AWS GovCloud and Azure Government resources, they can build a unified dashboard to visualize key latency metrics, container health, and API performance across the hybrid environment. When a latency spike threatens service delivery, Datadog automatically alerts them of a backend issue. The team quickly identifies and fixes the problem, restoring performance without disrupting the citizen experience.

Resolve security and performance issues faster

Quickly identifying and remediating security threats and vulnerabilities is vital in modern IT environments. Datadog integrates with each layer of your stack and so can detect potential attacks and misconfigurations across your code, infrastructure, production applications, endpoints, and more. Once you detect a problem, you can take action and remediate from within Datadog. For example, a DevOps engineer at a financial agency receives an alert for a suspicious login attempt. The engineer can investigate the login, block the IP address, and update access controls within minutes, all without leaving Datadog.

Datadog can detect potential attacks and misconfigurations across each layer of your stack.
Overview page for Datadog Security
Datadog can detect potential attacks and misconfigurations across each layer of your stack.

Control cloud costs

Understanding and managing total cost of ownership (TCO) is critical for public-sector IT teams facing strict budgets and procurement oversight. Datadog Cloud Cost Management helps agencies embed FinOps awareness into their development processes by giving engineers and analysts visibility into the costs of their services. Teams can easily use Datadog to monitor resource utilization and cost across compute, database, and storage layers and identify underutilized or misallocated infrastructure, all within a single platform. This enables them to find opportunities to rightsize workloads and reduce cloud spend.

Prepare for FedRAMP High authorization with Datadog

Datadog is already FedRAMP Moderate-authorized, and with its new FedRAMP High “In Process” designation, government teams can confidently begin preparing for high-impact workloads:

  1. Start monitoring today in Datadog’s FedRAMP Moderate region
  2. Align your environment with a vendor on the path to FedRAMP High
  3. Reduce Authority to Operate (ATO) delays by working with a proven, security-first platform
  4. Scale observability as your mission evolves

How FedRAMP High-authorized observability empowers government IT teams

RoleBenefit
CIO/CTOModernize faster, reducing tool sprawl
CISOStrengthen cloud security posture, streamline audits
DevOps/SREDetect, investigate, and resolve incidents
Program ManagerEnsure service uptime and performance transparency

Datadog empowers technical teams across agencies to meet operational goals without sacrificing security, compliance, or cost control.

Continuing our commitment to government security

The FedRAMP High In-Process milestone reflects the next step in Datadog’s commitment to the public sector. We are on the path to full FedRAMP High authorization and DoD IL5 readiness to support the most sensitive federal workloads.

Get started with Datadog for Government

Datadog’s observability platform is trusted by leading government agencies and public-sector teams. Contact our Federal Sales or SLED Sales team to get started with FedRAMP High. Or get started with a free 14-day trial.