Corelight | Breaking Cybersecurity News | The Hacker News

Despite the abundance of telemetry at analysts’ disposal, many security operations teams struggle to answer a few basic questions during incident investigation: What happened? What evidence do we have? How do we know we’re seeing it all, in context? Answering these questions requires teams to go beyond alerts, the most common basis for initial triage. But investigations (and their outcomes) require defensible evidence, not assumptions, which is what alerts tend to offer.  Alerts are becoming less useful as vulnerability discovery accelerates (a.k.a., the Mythos Era). Most organizations can’t investigate the volume of new findings with existing workflows. Even with increased automation, SecOps teams need validated evidence of active exploit and exposure, not more raw telemetry. As AI expedites both attacks and defense, security teams need to lay the groundwork that allows them to validate findings, understand attacker behavior, and stop suspicious traffic before it results in...

Ask a cybersecurity pro about Network Detection and Response (NDR) and you might still hear "Noisy," "Too much data." But ask the teams running NDR that includes agentic AI capabilities and you'll hear they're actually using it to catch threats earlier, triage faster, and chase fewer false positives. The old complaint lingers in part because reputations are sticky, and because NDR has evolved faster than the narrative. The origins of noise NDR deployments have always given analysts deep visibility into network traffic, encrypted session behavior, and protocol anomalies. But visibility often came as raw material, not finished intelligence. Some systems required extensive manual tuning during deployment to prevent SIEM overload. Organizations that couldn't invest that time (or didn't know how important it was) helped cement NDR's "alert firehose" or "noisy" reputation. NDR with agentic AI turns noise into narrative A...

Just as triathletes know that peak performance requires more than expensive gear, cybersecurity teams are discovering that AI success depends less on the tools they deploy and more on the data that powers them The junk food problem in cybersecurity Imagine a triathlete who spares no expense on equipment—carbon fiber bikes, hydrodynamic wetsuits, precision GPS watches—but fuels their training with processed snacks and energy drinks. Despite the premium gear, their performance will suffer because their foundation is fundamentally flawed. Triathletes see nutrition as the fourth discipline of their training that can have a significant impact on performance and can even determine race outcomes. Today's security operations centers (SOCs) face a similar issue. They're investing heavily in AI-powered detection systems, automated response platforms, and machine learning analytics—the equivalent of professional-grade triathlon equipment. But they're powering these sophistic...