
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
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 ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...
