Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

BitRAT is a potent and versatile Remote Access Trojan (RAT) commonly sold on underground forums. Its popularity stems from a robust feature set and an emphasis on stealth, allowing it to evade detection by hiding command-and-control (C2) communications over seemingly benign protocols. This makes traditional detection methods more challenging. By examining the subtle artifacts it leaves behind, even in encrypted traffic, defenders can expose these elusive threats.

A few years ago, I was part of a team responding to a high-profile security incident. After the incident was resolved, I was given a list of NDR rules to add to my firewalls. The issue was that the rules were not made for Suricata, the IDS I was using in this position at that time, so they generated false positives. With all that extra noise, I made it my goal to eliminate that excess noise.

The first time an MCP (Model Context Protocol) server felt real to me, it wasn't because of a clean demo. It was because of the noise. TL;DR: The harness matters more than the protocol, and the evidence matters more than both. MCP earns its keep when it shortens the path from a good security question to trustworthy evidence, and almost everything interesting about making that work happens in the harness wrapped around the model. In this series, I will cover how to build an MCP for an AI SOC.

At Corelight Labs, our mission is to help organizations stay a step ahead of evolving threats. When our researchers came across Censys' detailed write-up on ScoutC2, a rapidly growing open-source command-and-control (C2) framework favored by threat actors, we knew we needed to bolster community defenses quickly.

It was time to write another book. That’s what I thought when I heard that Corelight wanted to update its 2021 book on network detection and response (NDR). Tamara Crawford, who owned the project, scheduled a meeting with me and asked if I might be interested in helping, depending on who might write the text.

The first time an MCP (Model Context Protocol) server felt real to me, it wasn't because of a clean demo. It was because of the noise. TL;DR: The harness matters more than the protocol, and the evidence matters more than both. MCP earns its keep when it shortens the path from a good security question to trustworthy evidence, and almost everything interesting about making that work happens in the harness wrapped around the model. In this series, I will cover how to build an MCP for an AI SOC.

Adversaries are evolving faster than defenders can respond, and they're weaponizing AI to accelerate their attacks. We’ve seen “living-off-the-land”, lateral movement, and the abuse of legitimate administrator tools enable hackers to hide in plain sight, diluting the effectiveness of traditional detection methods. Meanwhile, defenders are nervously trying to keep up with the accelerating pace of AI-empowered threats hitting them at machine speed.

Corelight Sensor v29.1 and Fleet Manager v29.1.1 fundamentally expand what a Corelight Sensor delivers. The release turns existing network evidence into a shared source of truth for SecOps, NetOps, triage, and forensic investigation. Network performance monitoring and asset classification unlock new value from traffic you're already collecting.

Every packet flowing through a Corelight sensor contains both security-relevant data and performance-relevant data. Until now, Corelight has focused exclusively on extracting security value from network traffic: connection logs, protocol analysis, and threat detections. But the same traffic that reveals lateral movement also reveals TCP latency. The same DNS queries that surface potential C2 channels also reveal resolution timing.

There is a saying you will hear from veterans in the Black Hat Network Operations Center (NOC): “Threat hunting on the Black Hat network is like trying to find a needle in a stack of needles." With dozens of training classes running live exploit chains, capture-the-flag traffic, and researchers probing every corner of the internet, our Corelight sensors generate a rich set of Zeek logs, many of which can look suspicious in varying degrees.