Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

If you hear that a product is 92% effective, you’d assume it’s operating as intended. It seems like a success story. But dig a little deeper, and the picture changes; only 51% say that their security information and event management (SIEM) is very effective. What does it mean when a majority of security relies on a tool that works, but doesn’t work well enough? Not broken, not exceptional. It’s somewhere in between.

Everyone is building sophisticated intelligence layers with improved models and smarter agents to automate threat detection, investigation, and response. It’s what is needed in order to mature into an AI SOC. However, the organizations seeing the most value from AI in their SOC are not focusing solely on the intelligence layer. They’re focusing on the data foundation first.

Like the sands through the hourglass, so are the days of our SOC lives…. An alert surfaces, and while it doesn’t immediately signal a critical incident, it carries just enough ambiguity to require attention. An analyst opens the investigation, begins pulling in context, reviews authentication activity, pivots into endpoint data, and checks for any corresponding changes in the cloud environment.

The SOC was originally designed for a threat landscape that no longer exists. Today, the sheer number and speed of modern threats make it tough for even the best analysts to keep up. Manually sorting through huge amounts of data, dealing with alert fatigue, and relying on fixed rules make it harder to understand the full story behind each threat. The AI SOC addresses this problem, but not in the way most vendors describe. It’s not just a simple product or feature.

Tool proliferation is compounding. Alerts are multiplying faster than teams can triage them. Visibility gaps are hiding real threats. And security teams are stuck babysitting archaic security infrastructure, rather than detecting and stopping threats. Organizations across gaming, fintech, and retail are feeling the weight of traditional, on-premises SIEMs.

For two decades, security practitioners have lived with a tidy, almost childlike definition of an incident.

Across Europe and beyond, regulatory frameworks are reshaping how and where organizations manage data. These laws establish enforceable standards for data sovereignty, data governance, and data privacy that directly influence cloud architecture, security strategy, and AI innovation. Without these regulations, you run the risk of these organizational consequences: Data management shouldn’t be considered as only a task for IT. It’s a board-level priority.

Security threats have always been expanding and evolving, but recent data shows that modern applications are more complex for security and operations than ever before. And AI is only a piece of that puzzle. To stay on top of the changing market and hear directly from security leaders on what’s really top of mind, Sumo Logic surveyed over 500 security leaders with the help of UserEvidence. We asked about data pipelines, tool sprawl, confidence in SIEM, and, of course, AI.

Insider threats remain one of the most challenging security risks organizations face. Unlike external attackers who must breach perimeters, insiders already possess legitimate access to critical systems and data. They understand security controls, know where valuable assets reside, and can operate under the radar of traditional rule-based detection systems for extended periods.

If you’re like most companies we work with, you’re awash in opportunities (and a bit overwhelmed with pressure) to adopt AI. Of course, integrating new technologies means more data to manage and systems to monitor.