Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Security leaders spent most of the past year testing AI driven security automation. Many discovered that the promise of fully autonomous SOC operations collided with the reality of hallucinations, opaque recommendations, and inconsistent outcomes. McKinsey research now shows that more than 80 percent of organizations have not realized meaningful results from gen AI programs.

The evolution of your security stack is similar to the different phases of buying cars. In the beginning, you just need enough to transport a few items, maybe yourself and a few friends. The inexpensive two-door hatchback is perfect. However, as your family grows, whether with small humans or pets, you increasingly need more space and more capacity, leading to purchasing a four-door sedan or, even, a mini-van.

Most teams think of data lakes as cold storage. A long-term archive. A place to keep logs “just in case” while budgets tighten and ingest volumes rise. Functional, sure. But limited. The traditional data lake keeps everything, helps occasionally, and rarely fits the way analysts work. Graylog approaches the data lake differently. In Graylog 7.0, the data lake is not a warehouse. It is a pressure release valve for teams overwhelmed by storage cost, investigation delays, and cloud data sprawl.

In the original Star Trek television show, Captain Kirk would slightly recline in a command chair with various buttons that allowed him to deploy different technologies. Regardless of the alien threat, he had the necessary tools at his disposal to protect the Enterprise and his staff. An organization’s security operations center (SOC) acts as the Captain Kirk “command chair” for all security activities.

Every time you leave your home, you take various risks, like being in a car accident or being struck down by a meteor. In some cases, like the meteor, the likelihood of the event is so low as to be nearly nonexistent. In others, like the car accident, the likelihood might be higher. Similarly, every technology that you connect to your networks creates a cybersecurity security risk. Any device or application that connects to the public internet can be an entry point for attackers.

Security teams face an endless stream of alerts, false positives, and investigation backlogs. Every second counts, yet many AI-driven tools promise to handle everything for you that leaves analysts uncertain about how conclusions were made. Graylog takes a different path. The company develops assistive AI that helps analysts make faster, smarter calls with context, transparency, and control. No black boxes. No mystery logic.

Security operations are buried under too many tools. Analysts switch between consoles, piece together context by hand, and burn valuable hours reconciling data that should already work together. According to Gartner, security leaders use an average of 19 different tools, and 80% say this level of complexity creates blind spots. This fragmentation slows down detection and response, drives up costs, and wears out teams that are already stretched thin.

The obituary for SIEM has been written more than once. The latest headline from Dark Reading calls it “dying a slow death.” Catchy. But wrong. If you work in a SOC, you already know the need for centralized, contextualized visibility is not going anywhere. What is changing the future of SIEM, is how SIEM delivers it. If you are still thinking of SIEM as a clunky, high-cost log hoarder, you are stuck in the wrong decade.

The DFIR Report recently detailed a LockBit attack with ransomware intrusion that succeeded without advanced exploits or zero-day vulnerabilities. The attack relied on a stolen AnyDesk installer, credential reuse, and renamed PowerShell scripts that blended into routine activity. These moves were not sophisticated, but they were fast and effective. The end result: complete domain encryption.

There’s a quiet failure at the heart of many security programs. It’s not a lack of data. It’s too much of the wrong data. Telemetry pipelines built for volume, not visibility, now flood teams with noise instead of insight. The result? More alerts. Slower response. Overworked analysts are stuck maintaining ingestion rules instead of catching real threats.