Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

As an Acronis user, you understand the importance of safeguarding your digital life. Whether you're a photographer with an ever-growing library of high-resolution images, a videographer churning out terabytes of footage, a content creator managing countless projects or a power user with multiple virtual machines and a home lab, the need for ample, secure cloud storage is constant. You want one reliable place for all your data, without the hassle of juggling multiple tools and accounts.

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.

If you look at an enterprise architecture diagram from five years ago, it looks relatively tidy. You had a data center, maybe a cloud provider, and a few gateways. Today, that diagram looks like a constellation. Data is living in AI platforms like Databricks. Frontend applications are pushed to the edge on Netlify. Logic is scattered across microservices, serverless functions, and legacy IIS servers. For security teams, this fragmentation creates a massive headache: Blind Spots.

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.