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Datadog Launches Compliance Monitoring

Datadog announced Compliance Monitoring, a new product that can identify misconfigurations that cause compliance drift as soon as they occur.

Once problems are identified, Datadog Compliance Monitoring immediately notifies engineers, enabling them to proactively remediate any issues.

The increasing adoption of cloud platforms has resulted in a proliferation of new security risks: from compliance-sensitive workloads on public clouds that are deployed before security tooling can detect them, to developers and automated configuration tools accidentally misconfiguring a service that opens security vulnerabilities. Furthermore, developers and site reliability engineers are now being asked to secure the services they own despite having little prior experience or training in security techniques.

“As cloud infrastructure continues to become more dynamic and scales to meet demand, tracking configuration for compliance will become more challenging,” said Renaud Boutet, VP of Product at Datadog. “Datadog Compliance Monitoring provides full end-to-end visibility into cloud environments, allowing for continuous tracking of security configuration rules in a single, unified platform. When Datadog detects a compliance violation, DevSecOps teams will receive an alert that diagnoses the failure, lists the exposed assets and provides instructions on how to remediate it, quickly.”

Compliance Monitoring tracks the state of all cloud-native resources, such as security groups, storage buckets, load balancers, and Kubernetes. Key features include:

- Wide spectrum security observability: Compliance Monitoring rapidly discovers all assets and their configurations, and combines this asset information with the full telemetry of the Datadog platform. Observing misconfigurations in the context of other threats and application performance allows developers and security engineers to go from identifying a poorly configured service to diagnosing an attack in seconds.

- Continuous compliance posture: Datadog uses two methods to continuously assess the configuration of an environment. First, Datadog crawls cloud health services configuration, ingesting this data and analyzing it. Second, the Datadog agent collects local configuration information from servers and containers.

- Compliance Status Snapshot: Expert-built dashboards offer comprehensive snapshots of the adherence to common compliance frameworks and standards such as PCI DSS and CIS Benchmarks.

- Production-ready file integrity monitoring (FIM): Datadog’s single universal agent collects data from containers, Kubernetes clusters, and hosts so organizations can monitor runtime security as they move their workloads to the cloud.

- Easy custom governance policies: Datadog provides a simple WYSIWYG interface for users to build their own custom security and governance policies and reporting dashboards.

Datadog Compliance Monitoring is now available in beta within the Datadog platform.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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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 ...

Datadog Launches Compliance Monitoring

Datadog announced Compliance Monitoring, a new product that can identify misconfigurations that cause compliance drift as soon as they occur.

Once problems are identified, Datadog Compliance Monitoring immediately notifies engineers, enabling them to proactively remediate any issues.

The increasing adoption of cloud platforms has resulted in a proliferation of new security risks: from compliance-sensitive workloads on public clouds that are deployed before security tooling can detect them, to developers and automated configuration tools accidentally misconfiguring a service that opens security vulnerabilities. Furthermore, developers and site reliability engineers are now being asked to secure the services they own despite having little prior experience or training in security techniques.

“As cloud infrastructure continues to become more dynamic and scales to meet demand, tracking configuration for compliance will become more challenging,” said Renaud Boutet, VP of Product at Datadog. “Datadog Compliance Monitoring provides full end-to-end visibility into cloud environments, allowing for continuous tracking of security configuration rules in a single, unified platform. When Datadog detects a compliance violation, DevSecOps teams will receive an alert that diagnoses the failure, lists the exposed assets and provides instructions on how to remediate it, quickly.”

Compliance Monitoring tracks the state of all cloud-native resources, such as security groups, storage buckets, load balancers, and Kubernetes. Key features include:

- Wide spectrum security observability: Compliance Monitoring rapidly discovers all assets and their configurations, and combines this asset information with the full telemetry of the Datadog platform. Observing misconfigurations in the context of other threats and application performance allows developers and security engineers to go from identifying a poorly configured service to diagnosing an attack in seconds.

- Continuous compliance posture: Datadog uses two methods to continuously assess the configuration of an environment. First, Datadog crawls cloud health services configuration, ingesting this data and analyzing it. Second, the Datadog agent collects local configuration information from servers and containers.

- Compliance Status Snapshot: Expert-built dashboards offer comprehensive snapshots of the adherence to common compliance frameworks and standards such as PCI DSS and CIS Benchmarks.

- Production-ready file integrity monitoring (FIM): Datadog’s single universal agent collects data from containers, Kubernetes clusters, and hosts so organizations can monitor runtime security as they move their workloads to the cloud.

- Easy custom governance policies: Datadog provides a simple WYSIWYG interface for users to build their own custom security and governance policies and reporting dashboards.

Datadog Compliance Monitoring is now available in beta within the Datadog platform.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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 ...