When teams talk about “operational visibility,” they usually think about logs, SIEM data, metrics, or alerts. But there’s another dataset quietly powering everything beneath the surface: your infrastructure source-of-truth . For many organizations, that’s NetBox  — the authoritative registry for devices, racks, circuits, tenants, VLANs, VMs, and topology. But NetBox is not a search engine, not an analytics platform, and not designed for correlation across massive environments

Your SOC analysts are drowning in alerts. Each detection rule fires independently. A medium-severity alert here. A low-severity building block there. An ML anomaly over there. Individually, none of them screams "investigate immediately." But together? They might indicate a sophisticated attack in progress. The problem is, your analysts don't have time to manually correlate dozens of low and medium severity alerts across hosts and users. They're too busy triaging the high-seve

Using the Azure REST API to consume Sentinel alerts and incidents and send them to Elastic

Your organization has deployed EDR across all endpoints. Maybe you've even upgraded to XDR, correlating signals across endpoints, network, and cloud. Your coverage looks good. Your dashboards show green. You've got behavioral analytics, machine learning, automated response capabilities—the works. So you're secure, right? Not quite. Don't get us wrong: EDR/XDR is absolutely essential. If you don't have robust endpoint protection in modern threat landscape, you're already behin

You're designing your Elastic Security deployment architecture. You've got multiple domains, security zones, or network segments. The customers architect's logical thought process goes something like this: "We don't want agents from Domain A directly connecting to our Elasticsearch cluster. Let's put Fleet servers in each domain, combine them with Logstash for event forwarding, and create a nice segmented architecture." On paper, this looks clean. Agents stay in their domains

There's an interesting shift happening in Security Operations Centers right now. AI and machine learning are rapidly taking over tasks that, until recently, were the bread and butter of junior security analysts. Log normalization, correlation, initial triage, basic threat detection—the foundational work that every Tier 1 analyst cut their teeth on—is increasingly being handled by intelligent automation. On the surface, this sounds like an unqualified win. Who wouldn't want to

Walk into most SOCs and ask to see their detection strategy, and you'll often get one of two responses: a blank stare, or someone handing you a spreadsheet of detection rules sorted by severity. Here's the thing—a list of rules isn't a strategy. And without a real strategy, even the best detection engineering team is just guessing about what to build next. The Problem with Ad-Hoc Detection Engineering Most organizations approach detection engineering reactively. A new threat

Picture this: Your vulnerability scanner just finished its weekly run and flagged 347 critical vulnerabilities across your environment. Your patch management process kicks in. Tickets get created. Change requests get filed. Maintenance windows get scheduled. Your team starts the long slog of patching, testing, and deploying updates. Three months later, you're still working through that list. And this week's scan just found 289 new critical vulnerabilities. You're running on a

Here's a scenario that should make every security professional uncomfortable: A new CVE drops at 9 AM. By 11 AM, AI-assisted threat actors have analyzed the vulnerability, generated working exploit code, and begun scanning for vulnerable targets. By noon, the first attacks are underway. Meanwhile, your security team is still reading the advisory. This isn't a hypothetical future—it's happening right now. And it fundamentally changes the game we're playing. The New Math of Vul

Your organization just completed its annual penetration test. The report lands with 23 findings. Your team spends the next three months remediating. You retest, confirm fixes, and breathe a sigh of relief. Two weeks later, your development team pushes a new feature to production. DevOps makes infrastructure changes. A new cloud service gets spun up. Your attack surface just changed—but your security validation is done for the year. See the problem? The Point-in-Time Problem T

In today’s data-driven world, businesses face the challenge of managing vast amounts of information from multiple sources. The ability to collect, analyze, and visualize data quickly can make a significant difference in decision-making and operational efficiency. Elastic Stack offers a powerful solution to these challenges by providing a flexible platform for searching, analyzing, and visualizing data in real time. This post explores how businesses can unlock the full potenti

Security challenges grow every day as organizations face increasing threats from cyberattacks, data breaches, and insider risks. To protect sensitive information and maintain operational integrity, companies need tools that provide real-time visibility, fast detection, and effective response. Elastic Stack offers a powerful, flexible platform that helps security teams meet these demands by collecting, analyzing, and visualizing data from diverse sources. This post explores ho

In today’s digital world, security threats evolve rapidly. Organizations face constant challenges protecting their data, networks, and systems. Elastic Security consultancy services offer a powerful way to detect, investigate, and respond to cyber threats using the Elastic Stack. This guide explains how these services work, why they matter, and how businesses can benefit from expert support. Elastic Security dashboard showing real-time threat detection and analysis Elastic Se