SOC Automation | Breaking Cybersecurity News | The Hacker News

You invested in a new AI-SOC because you want your organization to be safe. You also don't want your SOC team to burn out from the flood of alerts they're receiving.  It's good at first. At deployment, the detections are lined up with your environment. Your SOC team reports it's going to be a learning curve, but it seems to be working. It's going well until a few months later, when it's not, at least not as well.  The problem is that the agent isn't processing alerts the way your team needs it to. It keeps flagging the CEO's logins as threats because it doesn't understand that he's traveling. It's also let a few real threats slip through the cracks. Threats that should have been easily caught. What's happening?  Pre-trained AI was built to recognize the familiar, and it does. It's trained on old data, old attack paths, and assumptions that made sense in the lab based on what's been observed before. What it can't do is understand the small, real-world details that an...

For nearly two decades, managed-security models have defined how most organizations handle detection and response. Faced with alert overload, chronic staffing shortages, and the high cost of 24/7 coverage, many teams turned to Managed Security Service Providers (MSSPs) and later to Managed Detection and Response (MDR) vendors to fill the gap. Beyond staffing and capacity, many also lacked in-house expertise in building detection systems. It was a rational choice. MSSPs and MDRs provided 24/7 monitoring, experienced analysts, and predictable coverage. They gave companies without an in-house SOC a viable way to maintain security coverage in an increasingly complex threat landscape. But the ground has shifted. AI-driven SOC platforms are now automating large parts of what human analysts once did: triaging alerts, correlating signals, enriching incidents, and recommending or even executing responses. That raises a simple but profound question: what happens to the managed-security m...