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Chainguard Partners with Datadog

Chainguard announced a partnership with Datadog. 

Together, Chainguard and Datadog will combine container observability with clear, prioritized actions to help engineering and security teams eliminate common vulnerabilities and exploits (CVEs), and improve software development velocity. Through a new Chainguard dashboard in Datadog, customers can gain real-time insights into container risks, receive clear remediation recommendations, and seamlessly transition to more secure alternatives — accelerating software delivery while reducing security threats.

The integration between Chainguard and Datadog enhances  Datadog's core container observability with new visibility and associated risk remediation potential, surfaced in a dashboard. The Chainguard dashboard organizes container metrics to understand where Chainguard is being used today and identifies environments where a more secure base image is available.

The dashboard will be available to all Datadog customers, offering a holistic view of existing container infrastructure and associated CVE risks, including:

  • Containers built using Chainguard images
  • Longest running container images
  • Vulnerabilities in most widely-used images
  • Chainguard alternatives for insecure container images

"Through our partnership with Datadog, we're combining leading observability with secure, minimal container solutions," said Kim Lewandowski, Chief Product Officer and Co-founder at Chainguard. "Chainguard is building the safe source for open source so customers can build more efficiently and securely from the start. Datadog is leading the way in observability and monitoring across cloud infrastructure. Together, we're empowering companies of all sizes to build software better."

"Tens of thousands of organizations rely on Datadog every day for real-time risk monitoring and visibility into the health and performance of their containerized environments," said Bharat Sajnani, Senior Vice President, Head of Corporate Development and Platform at Datadog. "With our integration with Chainguard, our customers can identify CVE risks within their container infrastructure, seamlessly pinpoint alternatives, and measure progress in implementing these minimal CVE container images. Together, we're helping companies make their container infrastructure more secure while making the most of their engineering resources."

With Chainguard and Datadog's integration, joint customers benefit from reduced risk across their application surface area. Now, security teams can move from reactive alerts to proactive risk reduction by identifying and prioritizing CVE remediation in their most widely deployed and high-risk containers. As a result, engineering teams will spend less time patching one-off containers, so organizations can redirect development resources and ship secure software faster.

The Chainguard and Datadog integration is now generally available. 

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Chainguard Partners with Datadog

Chainguard announced a partnership with Datadog. 

Together, Chainguard and Datadog will combine container observability with clear, prioritized actions to help engineering and security teams eliminate common vulnerabilities and exploits (CVEs), and improve software development velocity. Through a new Chainguard dashboard in Datadog, customers can gain real-time insights into container risks, receive clear remediation recommendations, and seamlessly transition to more secure alternatives — accelerating software delivery while reducing security threats.

The integration between Chainguard and Datadog enhances  Datadog's core container observability with new visibility and associated risk remediation potential, surfaced in a dashboard. The Chainguard dashboard organizes container metrics to understand where Chainguard is being used today and identifies environments where a more secure base image is available.

The dashboard will be available to all Datadog customers, offering a holistic view of existing container infrastructure and associated CVE risks, including:

  • Containers built using Chainguard images
  • Longest running container images
  • Vulnerabilities in most widely-used images
  • Chainguard alternatives for insecure container images

"Through our partnership with Datadog, we're combining leading observability with secure, minimal container solutions," said Kim Lewandowski, Chief Product Officer and Co-founder at Chainguard. "Chainguard is building the safe source for open source so customers can build more efficiently and securely from the start. Datadog is leading the way in observability and monitoring across cloud infrastructure. Together, we're empowering companies of all sizes to build software better."

"Tens of thousands of organizations rely on Datadog every day for real-time risk monitoring and visibility into the health and performance of their containerized environments," said Bharat Sajnani, Senior Vice President, Head of Corporate Development and Platform at Datadog. "With our integration with Chainguard, our customers can identify CVE risks within their container infrastructure, seamlessly pinpoint alternatives, and measure progress in implementing these minimal CVE container images. Together, we're helping companies make their container infrastructure more secure while making the most of their engineering resources."

With Chainguard and Datadog's integration, joint customers benefit from reduced risk across their application surface area. Now, security teams can move from reactive alerts to proactive risk reduction by identifying and prioritizing CVE remediation in their most widely deployed and high-risk containers. As a result, engineering teams will spend less time patching one-off containers, so organizations can redirect development resources and ship secure software faster.

The Chainguard and Datadog integration is now generally available. 

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Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...