Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

If mobile apps were high school stereotypes, social media would be the popular kid everyone gossips about, but secretly rolls their eyes at. Everyone uses them, everyone knows the risks, and yet everyone keeps showing up at their parties. In our consumer survey earlier this year, 56% of U.S. respondents said they trust social media apps the least with their personal data. Not banks, not e-commerce sites - social media.

The entire world of technology is abuzz about AI/ML. It’s arguably the most disruptive technology to society since the smartphone. In fact, Gartner estimates that the number of companies using open-source AI directly will increase tenfold by 2027.

If your company is shipping chatbots, copilots, or decision systems, you have probably heard the question many times: what is data privacy in AI, and how do we do it right. The answer is simpler than it looks. Data privacy in AI is a set of habits and controls that limit what personal or sensitive data you collect, how you use it, where you store it, and who can see it. When those habits are part of the build, AI products move faster, customers feel safer, and audits become routine.

The FBI and the American Bankers Association (ABA) have issued a joint advisory warning of the growing threat posed by AI-generated deepfake scams. “Criminals may pose as loved ones, government officials, law enforcement personnel, or even celebrities, often using fear and urgency to convince victims to send money or share sensitive information,” the advisory says.

The AI revolution is here, and it’s running on Kubernetes. From fraud detection systems to generative AI platforms, AI-powered applications are no longer experimental projects; they’re mission-critical infrastructure. But with great power comes great responsibility, and for Kubernetes platform teams, that means rethinking security.

If shadow IT was the SaaS era’s guilty pleasure, shadow AIT is GenAI’s sugar rush: unsanctioned AI models, tools, and autonomous agents quietly wired into business workflows—often without approvals, logging, or controls. It’s fast, it’s useful, and it can bite.

Every IT team is under pressure to “do more with AI.” A new tool promises smarter workflows, a new agent claims to replace manual tasks. But if you’re managing service requests, availability SLAs, patch cycles, infrastructure capacity, and application performance every day, you know the truth: AI doesn’t automatically reduce complexity on its own.