Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI-powered social engineering attacks are significantly more successful than traditional attacks, according to a new report from cyber risk management firm Resilience. The researchers state, “Social engineering attacks fueled 88% of material losses, with AI-powered phishing achieving a 54% success rate compared to just 12% for traditional attempts.” AI allows attackers to easily craft sophisticated phishing emails, as well as voice and video deepfakes.

Generative AI is transforming the way enterprises approach daily operations – powering virtual assistants, summarizing medical records, and aiding clinicians with insights. These benefits come at a cost: risk to a wide range of sensitive data in AI-driven workflows. Traditional access controls and content filters that work for static systems fail as these are not designed for the free-flowing, context-rich data exchanges in LLM applications.

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.

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.

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.

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.

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.