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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...

AI SOC Agents are going through a hype cycle. If we're going by Gartner's Hype Cycle for Security Operations, 2025 , this technology is still an "Innovation Trigger", but it's at the cusp of "Peak of Inflated Expectations". Every vendor claims their solution will revolutionize security operations. Every conference features another keynote promising autonomous defense. And every CISO is being asked whether AI will replace their security team. At Redis, implementing AI in the SOC has been more of a measured journey. The model is more of a hybrid SOC, so there's a combination of external service providers as well as internal resources. In this case, Prophet Security is currently proving themselves alongside a more traditional MDR provider.  But let's take a step back.  The Tipping Point for AI Adoption within the SOC Considering an AI solution for Redis' SOC came down to the confluence of three drivers.  On an individual level, there was more value from AI tools 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...

Cybersecurity is undergoing a necessary transformation from reacting to threats as they arise to proactively anticipating and addressing them through Threat-Informed Defense (TID) . This shift emphasizes operational discipline over accumulating more tools. It involves using threat intelligence to streamline existing technologies, enhance the quality of security signals, and focus efforts on the threats most relevant to each organization. The goal is to continuously identify and close security gaps by combining insights from external threat data with internal defense capabilities. How do you put TID into practice? The team at  Filigran has broken down the TID framework into a six-stage pipeline to develop actionable chunks for cybersecurity leaders. In this article, we share the details so that your security teams can leverage it too to support TID. What is Threat-Informed Defense? First advocated by  MITRE , Threat-Informed Defense (TID) leverages MITRE ATT&CK framewo...

For most of its history, the Security Operations Center (SOC) has been a privilege of the few. Building one meant millions in technology spend and round-the-clock analyst coverage. Unsurprisingly, for years, SOCs were a privilege of the few -  large enterprises and organizations with high-risk profiles, where budgets and scale justified the investment. Everyone else was left with partial coverage or had to outsource. That reality is changing. AI has flipped the SOC equation. What was once out of reach for all but the largest enterprises is now accessible and affordable for nearly every company that needs one. The risk every company faces By now, almost any 9-year-old knows that cyberattacks threaten every company . It's no longer just banks and financial giants in the crosshairs. Over the past decade, cyberattacks have expanded into every sector, from e-commerce sites to research institutes to local hospitals. Recent data from the 'VikingCloud 2025 SMB Threat Landscape' repo...

At some point in the last decade, SIEMs turned into that one friend who always promises to help you move, then shows up late, eats all your pizza, and still expects gas money. They were supposed to deliver centralized visibility and faster investigations. Instead, most SOC teams ended up with endless alerts, eye-watering bills, and dashboards that look impressive on the big screen but don't actually stop attackers. So, how did we end up here? A short history: when SIEMs were actually useful Back when firewalls were still exciting, SIEMs solved a real problem: logs scattered everywhere, auditors breathing down your neck, and no way to answer "who logged into what, when?" Then came the "next-gen" era. Vendors promised smarter detection, correlations across your stack, and even a pinch of threat intel. The promise was fewer false positives and a faster response. But instead of taming noise, NG SIEMs just amplified it. It was like turning up the volume on a broken radio and calling ...