Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For those of you who are like me, when I first heard about the new EU AI Act, I had flashbacks to the implementation of the General Data Protection Act (GDPR) back in 2018. There are certainly a lot of similarities with the EU leading the way in consumer protections that will likely lead to more, similar legislation across the globe. I’m also reminded of the iPhone when it was introduced in the consumer market and bled into the workplace (I for one held onto my Blackberry for as long as I could).

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.

For years, AI and machine learning workloads lived in the lab. They ran as internal experiments, batch jobs in isolated clusters, or offline data pipelines. Security focused on internal access controls and protecting the data perimeter. That model no longer holds. Today, AI models are increasingly part of production traffic, which is driving new challenges around securing AI workloads in Kubernetes.

Phishing attacks are everywhere these days. People encounter them while shopping, job hunting, reading work emails, and checking personal texts. Thanks to AI-powered scammers, phishing has become both more common and harder to spot, leading to disastrous consequences. A phishing attack on a business costs an average of $4.8 million, and attacks on individuals can drain bank accounts and wreck credit scores.