Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Picture a SOC analyst starting an investigation. A suspicious spike in authentication activity appears on their dashboard, and they need to understand what’s happening quickly. To do that, they move through a familiar sequence of tools. What begins as a single investigation quickly turns into a chain of context switches: That’s nine steps to investigate one event. This isn’t accidental. Security tools have evolved to solve isolated problems, but together they have created fragmentation.

Being a security analyst can feel like being trapped in a Where’s Waldo book. You can find yourself staring at a data stream looking for something that “isn’t like the others.” However, as your organization collects and correlates more data from the environment, finding the Waldo can feel overwhelming. In a modern IT environment, organizations have hundreds or thousands of devices, users, and data points that they need to correlate so they can identify normal network activity.

Whether pulling items together for a holiday dinner or prepping weekly meals, you need to have all the ingredients necessary to cook the meals you want to eat. Often, this means making a grocery list, checking off items as you take them from the shelves, and, possibly, grumbling when one of the items isn’t available. In the IT and business worlds, audit logging is the shopping list that helps organizations with compliance readiness.

Cybersecurity teams are no longer circling an AI bubble. Rather, they are staffing inside it, buying within it, and getting measured by it. This matters because bubbles create a predictable trap: expectations are set higher than teams truly can deliver. Cato Networks CEO Shlomo Kramer recently told Business Insider the market is experiencing an AI bubble driven by heavy investment and AI-driven profit improvements, which he expects to unwind. A correction will not pause attacker activity.

Security professionals often compare their jobs to a game of “Whack-a-Mole,” the arcade game where players try to hit little plastic moles on the head. The moles pop up in a randomly generated way, making it difficult to predict which one will show its little head next.

A recent University of North Carolina Wilmington study tested whether general-purpose large language models could infer CVSS v3.1 base metrics using only CVE description text, across more than 31,000 vulnerabilities. The results show measurable progress, but they also expose a hard limit that matters far more than model selection: Model quality helps, but missing context sets a ceiling on reliability.

Enterprise AI adoption moved fast. Speed mattered. Shipping mattered. Getting AI into production mattered. That phase is over. Security leaders are now asking a harder question: whether the AI already embedded in security operations is safe, explainable, and aligned with how modern SOC teams actually work. The focus has shifted from adoption to trust, specifically explainability, governance, and operational fit.

While Hamlet asked the existential question “to be or not to be,” most security teams ask an equally esoteric question that ultimately defines their ability to manage alerting and detection: “to deploy on-prem or in the cloud?” When adopting a security information and event management (SIEM) solution, organizations must make a foundational decision around whether to deploy the solution on-premises or in the cloud.

Most people know the old fairy tale of the boy who cried wolf. Every day, the little shepherd would scream from the top of his hill, “A wolf is chasing the sheep!” While villagers initially responded to the alarm, they soon realized that the boy was lying to them. In the end, when a wolf truly did chase the sheep, no one heeded the boy’s cry.

Security operations teams are under sustained pressure. Alert volumes continue to rise, environments grow more distributed, and experienced analysts remain scarce. Much of the industry conversation around AI focuses on autonomy and fully automated response. That focus skips the most reliable efficiency gains available right now.