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Datadog for Government Achieves FedRAMP High "In Process" Status

Datadog is advancing toward Federal Risk and Authorization Management Program (FedRAMP) High authorization, which will ultimately enable federal agencies to more effectively monitor, secure and optimize their critical applications and infrastructure while adhering to stringent compliance frameworks.

US federal agencies are required to meet rigorous security and compliance standards. Datadog for Government previously achieved Moderate-Impact authorization through agency sponsorship. By pursuing FedRAMP High authorization, Datadog can better align with the federal government’s modernization and digital transformation initiatives, and prepare government IT leaders and engineers to leverage Datadog’s full-stack and unified platform for observability and security.

“Tool sprawl, siloed data and limited visibility across complex environments remain common problems for federal agencies,” said Yrieix Garnier, VP of Product at Datadog. “Datadog’s unified platform is uniquely positioned to solve these problems for organizations. Today’s announcement builds on our commitment to the U.S. public sector and is another milestone to providing the highest level of cloud security and observability for government agencies.”

Being "In Process" means that Datadog is actively working towards achieving full FedRAMP authorization at the High impact level, which requires stringent security controls for protecting highly sensitive data. This status signifies Datadog has completed a readiness assessment and is undergoing the process of gaining an Authority to Operate (ATO) from an agency sponsor. Datadog for Government is working to achieve FedRAMP High authorization in the second half of this year.

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Datadog for Government Achieves FedRAMP High "In Process" Status

Datadog is advancing toward Federal Risk and Authorization Management Program (FedRAMP) High authorization, which will ultimately enable federal agencies to more effectively monitor, secure and optimize their critical applications and infrastructure while adhering to stringent compliance frameworks.

US federal agencies are required to meet rigorous security and compliance standards. Datadog for Government previously achieved Moderate-Impact authorization through agency sponsorship. By pursuing FedRAMP High authorization, Datadog can better align with the federal government’s modernization and digital transformation initiatives, and prepare government IT leaders and engineers to leverage Datadog’s full-stack and unified platform for observability and security.

“Tool sprawl, siloed data and limited visibility across complex environments remain common problems for federal agencies,” said Yrieix Garnier, VP of Product at Datadog. “Datadog’s unified platform is uniquely positioned to solve these problems for organizations. Today’s announcement builds on our commitment to the U.S. public sector and is another milestone to providing the highest level of cloud security and observability for government agencies.”

Being "In Process" means that Datadog is actively working towards achieving full FedRAMP authorization at the High impact level, which requires stringent security controls for protecting highly sensitive data. This status signifies Datadog has completed a readiness assessment and is undergoing the process of gaining an Authority to Operate (ATO) from an agency sponsor. Datadog for Government is working to achieve FedRAMP High authorization in the second half of this year.

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Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

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The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

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