Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Modern applications generate a constant stream of logs, some of which carry more information than they should. For too many organizations, logs include personally identifiable information (PII) such as customer names that were never meant to leave production systems. Teams try to limit this data exposure by using regular expressions to detect and obfuscate matches, only to discover that names like John O’Connor, Mary-Jane, Jane van der Meer, and A. García slip through.

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.

Managed security service providers (MSSPs) deliver 24/7 monitoring and incident response for hundreds of customers across large, hybrid environments. As they add more customers and ingest more logs, MSSPs face mounting difficulties in collecting and processing that data before routing it to downstream security tools. Doing this reliably at petabyte scale while accounting for complex, customer-specific taxonomy and compliance requirements is a major challenge.

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.