Yesterday, I had a short presentation on netlab use cases during the NetBCN event. It covered a dozen examples, from rapid prototyping to testing network automation software and arguing with vendor TAC. I added the “use cases” part of the presentation to the standard netlab presentation; you can view the results on ipSpace.net (no account or registration required).
When I first started learning Kubernetes, I had the idea that observing the network traffic between a client system using kubectl and the Kubernetes API Server would be a useful thing to do. The source of the idea is unclear; I am unsure why I thought this would be useful as a learning tool. Regardless, I continued on with learning Kubernetes and never really pursued this idea—until this week. I found it can be a useful troubleshooting technique, but I will leave it up to you to determine if it is a useful learning technique. In this post, I will show you how to observe kubectl traffic using mitmproxy.
This technique is inspired by/informed by Ahmet Alp Balkan’s similarly-named blog post from 2019. Unfortunately, I found the instructions there to be incomplete (most likely just due to the passage of time and continued evolution of the tools involved).
I used the following tools and environments in my testing:
apt.kubectl version 1.33.3 was used to communicate to a self-managed cluster Continue readingAI Powered Intelligence, Unified Traffic Observability and Scalable Infrastructure Management
As anyone managing one or more Kubernetes clusters knows by now, scaling can introduce an exponentially growing number of problems. The sheer volume of metrics, logs and other data can become an obstacle, rather than an asset, to effective troubleshooting and overall cluster management. Fragmented tools and manual troubleshooting processes introduce operational complexity leading to the inevitable security gaps and extended downtime. As the number of clusters grows it becomes more important than ever to find ways of reducing the observability noise, decluttering the monitoring stack and eliminating the bottlenecks that get in the way of keeping your clusters stable and secure.
The Winter 2026 release of Calico Enterprise and Calico Cloud addresses the pain points of scaling clusters with three key enhancements:
AI Assistant for Calico: Efficiently navigate disparate data sources to quickly get answers through natural language, or proactively identify problems before they arise.
I decided it was high time to create EVPN/MPLS netlab integration tests and wanted to use the same approach I used for the EVPN/VXLAN ones:
This is the graph netlab created from the lab topology:
For years, the cybersecurity industry has suffered from a "data gravity" problem. Security teams are buried under billions of rows of telemetry, yet they remain starved for actionable insights.
A Threat Intelligence Platform (TIP) is a centralized security system that collects, aggregates, and organizes data about known and emerging cyber threats. It serves as the vital connective tissue between raw telemetry and active defense.
The underlying architecture of Cloudflare’s Threat Intelligence Platform sets it apart from other solutions. We have evolved our Threat Intelligence Platform to eliminate the need for complex ETL (Extract, Transform, Load) pipelines by using a sharded, SQLite-backed architecture. By running GraphQL directly on the edge, security teams can now visualize and automate threat response in real time. Instead of one massive database, we distribute Threat Events across thousands of logical shards — meaning sub-second query latency, even when aggregating millions of events across global datasets.
By unifying our global telemetry with the manual investigations performed by our analysts, our intelligence platform creates a single source of truth that allows security teams to move from observing a threat to preemptively blocking it across the Cloudflare network. We believe your intelligence platform shouldn't just tell you that Continue reading
Today’s threat landscape is more varied and chilling than ever: Sophisticated nation-state actors. Hyper-volumetric DDoS attacks. Deepfakes and fraudsters interviewing at your company. Even stealth attacks via trusted internal tools like Google Calendar, Dropbox, and GitHub.
After spending the last year translating trillions of network signals into actionable intelligence, Cloudforce One has identified a fundamental evolution in the threat landscape: the era of brute force entry is fading. In its place is a model of high-trust exploitation that prioritizes results at all costs. In order to equip defenders with a strategic roadmap for this new era, today we are releasing the inaugural 2026 Cloudflare Threat Report. This report provides the intelligence organizations need to navigate the rise of industrialized cyber threats.
Cloudforce One has observed a broader shift in attacker psychology. To understand how these methods win, we have to look at the why behind them: the Measure of Effectiveness, or MOE.
In 2026, the modern adversary is trading the pursuit of "sophistication" (complex, expensive, one-off hacks) in favor of throughput. MOE is the metric attackers use to decide what to exploit next. It is a cold calculation of the Continue reading
Bruce Davie published a nice article explaining why it makes little sense to use an algorithm that’s supposedly faster than Dijkstra’s in link-state routing protocols.
Other interesting data points from the article (and linked presentations):
It turns out (as I expected) that all the noise about the need for new routing protocols we were experiencing a few years ago was either due to bad implementations or coming from nerds looking for new toys to play with.
Today’s security ecosystem generates a staggering amount of complex telemetry. For instance, processing a single email requires analyzing sender reputation, authentication results, link behavior, infrastructure metadata, and countless other attributes. Simultaneously, Cloud access security broker (CASB) engines continuously scan SaaS environments for signals that detect misconfigurations, risky access, and exposed data.
But while detections have become more sophisticated, explanations have not always kept pace.
Security and IT teams are often aware when something is flagged, but they do not always know, at a glance, why. End users are asked to make real-time decisions about emails that may impact the entire organization, yet they are rarely given clear, contextual guidance in the moment that matters.
Cloudy changes that.
Cloudy is our LLM-powered explanation layer, built directly into Cloudflare One. It translates complex machine learning outputs into precise, human-readable guidance for security teams and end users alike. Instead of exposing raw technical signals, Cloudy surfaces the reasoning behind a detection in a way that drives informed action.
For Cloudflare Email Security, this means helping users understand why a message was flagged before they escalate it to the security operations center, or SOC. For Cloudflare CASB, it means helping administrators quickly understand Continue reading
Email security has always been defined by impermanence. It is a perpetual call-and-response arms race, where defenses are only as strong as the last bypass discovered and attackers iterate relentlessly for even marginal gains. Every control we deploy eventually becomes yesterday’s solution.
What makes this challenge especially difficult is that our biggest weaknesses are, by definition, invisible.
This problem is best illustrated by a classic example from World War II. Mathematician Abraham Wald was tasked with helping Allied engineers decide where to reinforce bomber aircraft. Engineers initially focused on the bullet holes visible on planes returning from missions. Wald pointed out the flaw: they were reinforcing the areas where planes could already take damage and survive. The true vulnerabilities were on the planes that never came back.
Email security faces an identical hurdle: our detection gaps are unseen. By integrating LLMs, we advance email phishing protection and move from reactive to proactive detection improvement.
The limits of reactive defense
Traditional email security systems improve primarily through user-reported misses. For example, if we marked a spam message as clean, customers can send us the original EML to our pipelines for our analysts to analyze and update our models. This feedback loop Continue reading
Starting today, Cloudflare CASB customers can do more than see risky file-sharing across their SaaS apps: they can fix it, directly from the Cloudflare One dashboard.
This launch marks a huge advancement for Cloudflare’s Cloud Access Security Broker (CASB). Since its release, Cloudflare’s API-based CASB has focused on providing robust, comprehensive visibility and detection. It also connects to the SaaS tools your business runs on, surfacing misconfigurations, and flagging overshared data before it becomes tomorrow’s incident.
With today’s release of Remediation – a new way to fix problems with just a click, right from the CASB Findings page – CASB begins its next chapter, and moves from telling you what’s wrong to helping you make it right.
An example of a Remediation Action (Remove Public File Sharing) in a CASB Finding.
Inside Cloudflare One, our SASE platform, CASB connects to the SaaS and cloud tools your teams already use. By talking to providers over API, CASB gives security and IT teams:
A consolidated view of misconfigurations, overshared files, and risky access patterns across apps like Microsoft 365, Google Workspace, Slack, Salesforce, Box, GitHub, Jira, and Confluence (CASB Integrations).
The MikroTik hAP ax³ is the latest addition to MikroTik's family of dual-band wireless routers. […]
The post Initial Setup of MikroTik hAP ax³ Router first appeared on Brezular's Blog.
Like perhaps some readers, I am quite particular about what gets installed on my systems. I try to keep my systems as “clean” as possible, doing my best to avoid tools that have an extensive list of dependencies that must be installed and updated. Where that isn’t possible—such as with the Azure CLI, which has a massive number of Python modules that are required in order for the tool to function—I will use various isolation mechanisms. For the Azure CLI, that’s typically been a Python virtual environment. Somewhat recently, though, I had an idea to try using a container. In this post, I’ll share what worked and what did not work when trying to run the Azure CLI in a container.
First, though, a disclaimer: I am not an Azure expert, nor am I a Python expert. I know enough to get by. If I share something here that’s incorrect, please contact me and constructively show me my errors so that I can fix them.
Before I started down this path, I was sure this would be a slam dunk. I mean, this is what containers are for, right? If you do some web searches for running the Azure CLI Continue reading
Someone recently asked me whether it’s possible to use netlab to build an MPLS/VPN (technically, BGP/MPLS IP VPN) lab with SR-MPLS core. Of course, let’s build a simple lab using Arista EOS and Linux containers to implement this topology:
Lab topology
Here’s the lab topology we’ll use (also available on GitHub):
Figure 9-4 illustrates a simplified receive-side processing pipeline, starting from the moment a Packet Header Vector (PHV), constructed by the Rx IFG, is delivered to the Receive Network Processing Unit (Rx NPU).
When the PHV arrives at the Rx NPU, it is dispatched to one of the Run-to-Completion (RTC) cores in the Packet Processing Array (PPA). Each RTC core processes the packet within a single execution context, allowing parsing, classification, lookup, and queuing decisions to be resolved without intermediate handoffs between processing stages.
The first task of the RTC parser is to perform deep inspection of the packet headers. While the Rx IFG has already extracted basic Layer-2 and Layer-3 information, the RTC parser determines whether the packet is tunneled and whether the switch itself is the tunnel termination point. To demonstrate this behavior, consider a VXLAN-encapsulated packet. The outer Ethernet and IP headers are used to forward the packet through the underlay network. If the outer destination IP address matches one of the local switch IP addresses, the device identifies itself as the tunnel endpoint. The tunneling protocol is recognized by examining the UDP header, where destination port 4789 indicates VXLAN. After the Continue reading
Return to office has stalled for many, and the “new normal” for what the corporate network means is constantly changing. In 2026, your office may be a coffee shop, your workforce includes autonomous AI agents, and your perimeter is wherever the Internet reaches. This shift has forced a fundamental change in how we think about security, moving us toward a critical new architecture: agile SASE.
For too long, organizations have struggled under a 'fragmentation penalty,' juggling a patchwork of legacy hardware and Virtual Private Network (VPN) concentrators. These tools don't just require massive upfront investment; they create a mountain of technical debt — the cumulative cost of maintaining thousands of conflicting firewall rules, manual patches, and aging hardware that can’t support AI-scale traffic.
First-generation SASE providers promised a cure, but often just moved the mess to the cloud. By treating every data center as an isolated island, they’ve replaced hardware silos with operational silos. The result isn't a lack of visibility, but a lack of actionability: plenty of data, but no single way to enforce a consistent policy across a borderless enterprise.
Our customers have told us they need an agile and composable platform. This week, we are announcing Continue reading
In the world of cybersecurity, "starting from scratch" is a double-edged sword. On one hand, you have a clean slate; on the other, you face a mountain of configurations, best practices, and potential "gotchas."
While Cloudflare One has been often cited as one of the easiest-to-use SASE platforms, there is no magic without proper configuration. And while Cloudflare has been striving to simplify complex networking concepts by creating products such as Cloudflare WAN, Magic Transit, and Cloudflare Network Firewall, which simplify and reduce the typical complexity associated with deploying comparable functions from other vendors, the breadth of capabilities provided by Cloudflare One require creation of best-practice policies and templates to achieve the most optimal outcomes.
To make it easy to start taking advantage of Cloudflare’s powerful SASE platform, we have developed a method that ensures customers get the right configuration quickly and easily. We call it Project Helix.
In this post, we’ll dig into the problem of getting the correct customization, and how we built Project Helix to make it simple. That means our customers have access to the most powerful SASE platform out there — and the easiest to onboard.