Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

DDI Central version 6.1 introduced significant enhancements to the IPAM section, bringing a segmented view for sites, clusters, and supernets, along with multiple display options: table, tree, and card views. The release also added trusted feed configurations, root hint templates, and unmapped subnet monitoring, giving network admins greater flexibility and control over their DNS and DHCP resources.

The cybersecurity industry has long relied on a simple idea: find vulnerabilities, patch them, and measure success by how fast you close the gap. “Time-to-patch” became a badge of honor. That model no longer holds. The rise of Mythos-class Frontier AI Models introduces a different kind of threat. AI-driven, agentic attacks operate continuously, discover weaknesses automatically, and execute at a scale no human team can match.

Defenders have long known that richer evidence improves security outcomes by enabling faster triage, deeper analysis, and more complete investigation. Although Corelight was founded on this premise, it’s been hard for us to quantify the impact of better network data - until now. Recently, we built an agentic test harness to measure the success of frontier LLMs in responding to real-world attack scenarios, using a range of source data.

Relying on legacy hardware for modern business is like trying to run a high-speed bullet train on old wooden tracks. The train has immense potential, but the underlying foundation simply can’t support the speed, capacity, or safety required for the journey. As companies migrate to the cloud and adopt artificial intelligence, their networks must evolve. Outdated systems create bottlenecks that drain resources and expose sensitive data to modern threats.

In April 2026, Cato CTRL identified and blocked an attempted intrusion against a global manufacturing customer involving TencShell, a previously undocumented, Go-based implant derived from the open-source Rshell C2 framework. The activity appeared in traffic associated with a third-party user connected to the customer environment.