Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Synthetic data for AI development has become the default shortcut for most engineering teams. It’s fast, sidesteps privacy headaches, and lets you move without touching production. I get why teams default to it. But there’s a problem: synthetic data for AI routinely breaks down the moment your system hits real-world enterprise data. The system demos great. It passes every internal test. Then it lands in production and falls apart in ways you didn’t see coming.

An AI guardrail failure doesn’t come with a warning. One minute, a response goes out. Next minute, it’s a screenshot in the wrong hands, and the question isn’t how it happened. It’s why nobody had defined what the model was allowed to do in the first place. Most teams never asked what the model was actually permitted to do. Deployment happens fast. AI data privacy and leakage prevention aren’t configuration tasks.

Your database stores a credit card number: 4532 1234 5678 9010. You encrypt it for security. Now it looks like this: %Xk92@!mQz#Lp&7. Problem. Your payment system can’t process that. It expects a 16-digit number. Your billing software breaks. Your downstream analytics fail. Your whole pipeline comes to a halt. This is the exact problem that format-preserving encryption was built to solve.

Modern managed service providers (MSPs) are increasingly adopting RMM AI tools — remote monitoring and management software enhanced with artificial intelligence — to keep pace with growing IT demands. Traditional RMM platforms allow MSPs to remotely monitor client endpoints, deploy patches, run scripts and troubleshoot issues from a central console. Now, AI-powered RMM software is taking this a step further.

As software systems grow more complex, managing the development process becomes mandatory. The Software Development Life Cycle provides a structured standard that helps teams build, test, and maintain reliable applications.

In 2026, software development speed is an AI-solved problem. Yet, as AI-generated code volumes surge, organizations face a new kind of risk visibility gap. Developers are increasingly copying third-party snippets into their codebases—from both AI prompts and open-source software components—creating large security and compliance blind spots that lead to significant risks.

The KhangNghiem/fast-draft extension, listed on open-vsx.org/extension/KhangNghiem/fast-draft and now sitting above 26,000 downloads, had multiple malicious releases that execute a GitHub-hosted downloader and pull a second-stage RAT and infostealer from the BlokTrooper/extension repository. The confirmed malicious releases in the version line we inspected are 0.10.89, 0.10.105, 0.10.106, and 0.10.112.

Attending Mobile World Congress (MWC) 2026 in Barcelona was once again an incredible experience. Each year the event seems to evolve, and 2026 showcased just how quickly the telecom and cloud infrastructure landscape is changing. The show floor was packed, the conversations were engaging, and the innovations on display reinforced the pace at which our industry is moving.

Many IT professionals struggle to integrate artificial intelligence (AI) into their existing environments. You often find expensive hardware trapped in isolated clusters or dedicated hosts. Your infrastructure team manages access through manual ticket queues, which leads to low utilization and frustrating bottlenecks for developers. When you don’t have a standardized way to share and monitor accelerator resources, every hardware change risks downtime for your critical applications.

A surge in shipping-related phishing scams is targeting the Middle East and Africa (MEA) region, according to researchers at Group-IB. “To deliver the scam, the attacker sends a phishing link to victims via SMS using various spoofing or bulk-message techniques,” the researchers write. “These links are typically optimized for mobile devices, since most victims open SMS messages on their phones.