Skip to main content

Datadog Cloud Security Platform Launched

Datadog announced the launch of the Datadog Cloud Security Platform, adding full-stack security context to Datadog’s deep observability capabilities.

This new offering enables organizations to use a single platform to correlate security insights with monitoring data across infrastructure, network and application tiers, providing Security teams with the visibility they need to understand and respond to potential threats faster.

Datadog’s Cloud Security Platform enables DevOps and Security teams to access a shared source of truth supported by a common data model. With Datadog, in parallel to detecting potential threats, Security leaders now have access to the underlying infrastructure, network and application data at the time of an attack, meaning they have deeper insights that enable more accurate threat detection and accelerated incident response. And, unlike point solutions, Datadog’s platform approach ensures that this data is automatically correlated and presented in context, without requiring manual analysis.

“As organizations embark on their digital transformation journey, unifying once disparate security, compliance and engineering practices has become a key requirement to deliver best-in-class customer experiences,” said Amit Agarwal, CPO, Datadog. “Built for cloud scale, the Datadog Cloud Security Platform supports organizations in adopting a modern DevSecOps practice that will enable a more holistic and, ultimately, a more robust approach to security, without increasing the operational burden of deploying and maintaining multiple, disconnected point solutions.”

The Datadog Cloud Security Platform includes:

- Cloud Security Posture Management (CSPM) makes it easy to track whether your production environment complies with industry standards, such as PCI DSS, SOC 2 and HIPAA, and catches misconfigurations that leave your organization vulnerable to potential attacks.

- Cloud Workload Security (CWS) detects threats to your production workloads by monitoring file and process activity across your environments to help catch host and infrastructure-based attacks.

- Security Monitoring identifies threats to your cloud environments by analyzing operational and security logs. As an easy-to-use cloud-native SIEM, Security Monitoring provides out-of-the-box security integrations and threat detection rules that are easy to extend and customize.

- Application Security, currently in beta, provides protection against application-level threats by identifying and blocking attacks that target code-level vulnerabilities, such as SQL injections and cross-site scripting (XSS) exploits.

- Unified Observability and Security Reporting allows seamless pivots between DevOps telemetry and security insights. This unified experience enables Security teams to understand the operational and business impact of security incidents, and DevOps teams to see security signals alongside the metrics, traces and logs of their services.

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Datadog Cloud Security Platform Launched

Datadog announced the launch of the Datadog Cloud Security Platform, adding full-stack security context to Datadog’s deep observability capabilities.

This new offering enables organizations to use a single platform to correlate security insights with monitoring data across infrastructure, network and application tiers, providing Security teams with the visibility they need to understand and respond to potential threats faster.

Datadog’s Cloud Security Platform enables DevOps and Security teams to access a shared source of truth supported by a common data model. With Datadog, in parallel to detecting potential threats, Security leaders now have access to the underlying infrastructure, network and application data at the time of an attack, meaning they have deeper insights that enable more accurate threat detection and accelerated incident response. And, unlike point solutions, Datadog’s platform approach ensures that this data is automatically correlated and presented in context, without requiring manual analysis.

“As organizations embark on their digital transformation journey, unifying once disparate security, compliance and engineering practices has become a key requirement to deliver best-in-class customer experiences,” said Amit Agarwal, CPO, Datadog. “Built for cloud scale, the Datadog Cloud Security Platform supports organizations in adopting a modern DevSecOps practice that will enable a more holistic and, ultimately, a more robust approach to security, without increasing the operational burden of deploying and maintaining multiple, disconnected point solutions.”

The Datadog Cloud Security Platform includes:

- Cloud Security Posture Management (CSPM) makes it easy to track whether your production environment complies with industry standards, such as PCI DSS, SOC 2 and HIPAA, and catches misconfigurations that leave your organization vulnerable to potential attacks.

- Cloud Workload Security (CWS) detects threats to your production workloads by monitoring file and process activity across your environments to help catch host and infrastructure-based attacks.

- Security Monitoring identifies threats to your cloud environments by analyzing operational and security logs. As an easy-to-use cloud-native SIEM, Security Monitoring provides out-of-the-box security integrations and threat detection rules that are easy to extend and customize.

- Application Security, currently in beta, provides protection against application-level threats by identifying and blocking attacks that target code-level vulnerabilities, such as SQL injections and cross-site scripting (XSS) exploits.

- Unified Observability and Security Reporting allows seamless pivots between DevOps telemetry and security insights. This unified experience enables Security teams to understand the operational and business impact of security incidents, and DevOps teams to see security signals alongside the metrics, traces and logs of their services.

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...