Skip to main content

Automox Launches Real-Time Linux CVE Data to Outpace NVD Delays

Automox announced the launch of best-in-class Linux CVE data monitoring designed to provide timely, accurate, and comprehensive Common Vulnerabilities and Exposures (CVE) data for enterprise IT and security teams.

Identifying, prioritizing, and resolving vulnerabilities is critical to protecting business operations. But the National Vulnerability Database (NVD) struggles to keep pace, with over 30,000 CVEs still to be analyzed. This lag leaves organizations exposed to risks they cannot ignore. Automox’s new vulnerability data goes beyond NVD to close the gap, offering real-time CVE coverage to help teams fix more vulnerabilities, faster with:

  • Real-Time CVE Data: Gain access to up-to-date, actionable vulnerability data without relying solely on slower NVD timelines.
  • Expanded Operating System Coverage:  In addition to the existing support for Ubuntu, Debian, and Red Hat, CVE monitoring has been added for CentOS and Amazon Linux, with upcoming support for Oracle Linux, Alma, Fedora, SUSE, and more.
  • Context-Rich Vulnerability Insights: Adopt risk-based patching for your entire organization, on any operating system, using real-time CVEs instead of waiting for NVD analysis.

"Given the growing volume of vulnerabilities and the increasing delays in published CVE analysis, IT and security teams need faster, more actionable insights,” said Jason Kikta, CISO and SVP of Product at Automox. “Our new vulnerability data helps businesses prioritize new CVEs an average of 14 days faster than NIST’s NVD, then mitigate in accordance with their risk policies to maintain a robust security posture. We believe the future of endpoint management and patching is intelligent. To pave the way for that future, we intend to enable context-rich vulnerability insights across business-critical software so teams can focus on truly urgent tasks in a sea of noise.”

By prioritizing speed, accuracy, and cross-platform vulnerability data, Automox equips organizations with the tools needed to stay ahead of evolving threats. 

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Automox Launches Real-Time Linux CVE Data to Outpace NVD Delays

Automox announced the launch of best-in-class Linux CVE data monitoring designed to provide timely, accurate, and comprehensive Common Vulnerabilities and Exposures (CVE) data for enterprise IT and security teams.

Identifying, prioritizing, and resolving vulnerabilities is critical to protecting business operations. But the National Vulnerability Database (NVD) struggles to keep pace, with over 30,000 CVEs still to be analyzed. This lag leaves organizations exposed to risks they cannot ignore. Automox’s new vulnerability data goes beyond NVD to close the gap, offering real-time CVE coverage to help teams fix more vulnerabilities, faster with:

  • Real-Time CVE Data: Gain access to up-to-date, actionable vulnerability data without relying solely on slower NVD timelines.
  • Expanded Operating System Coverage:  In addition to the existing support for Ubuntu, Debian, and Red Hat, CVE monitoring has been added for CentOS and Amazon Linux, with upcoming support for Oracle Linux, Alma, Fedora, SUSE, and more.
  • Context-Rich Vulnerability Insights: Adopt risk-based patching for your entire organization, on any operating system, using real-time CVEs instead of waiting for NVD analysis.

"Given the growing volume of vulnerabilities and the increasing delays in published CVE analysis, IT and security teams need faster, more actionable insights,” said Jason Kikta, CISO and SVP of Product at Automox. “Our new vulnerability data helps businesses prioritize new CVEs an average of 14 days faster than NIST’s NVD, then mitigate in accordance with their risk policies to maintain a robust security posture. We believe the future of endpoint management and patching is intelligent. To pave the way for that future, we intend to enable context-rich vulnerability insights across business-critical software so teams can focus on truly urgent tasks in a sea of noise.”

By prioritizing speed, accuracy, and cross-platform vulnerability data, Automox equips organizations with the tools needed to stay ahead of evolving threats. 

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...