Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

2025 exposed a shift that had been forming for years. Security operations were not slowed by limited visibility or weak tooling. They were slowed because the effort required to interpret growing volumes of data increased faster than staffing, budgets, or governance frameworks could support. Alert queues expanded, dashboards multiplied, cloud bills shaped retention choices, and AI arrived before most organizations had clear policies to supervise it. It was not a talent problem.

Every cloud service provider that seeks an authorization to operate with the federal government using the FedRAMP framework has to undergo and pass an audit. Beyond passing the audit, the CSP needs to keep and maintain proof of not just their external audit, but also internal audits, continuous monitoring results, and more.

The Ransomware-as-a-Service (RaaS) ecosystem has changed the look and shape of modern day ransomware attacks. Malicious actors typically view their cybercrimes as a business, hoping to make the most amount of money with the least amount of effort. For example, according to research, AI-automated phishing attacks performed similarly to human generated ones and 350% better than the ones sent to the control group.