Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The term "AI Agent" is failing us. In Prediction, Ev warns that our vocabulary is lagging behind the technology. Calling everything an "AI Agent" is like calling everything "software." It’s too broad to be useful. A browser plugin has a completely different architecture than a microservice or a factory robot. They have different identities, different risks, and different security needs. You can't secure what you can't specifically identify.

Can "lesser-known" AI models actually keep up with the giants like Google, OpenAI, and Anthropic? In today’s video, we put MiniMax M2.1 to the ultimate test: building a production-ready, secure Node.js note-taking application from a single prompt. We’ll explore how to access MiniMax natively in the Windsurf IDE, walk through the debugging process for common errors (like environment variables and OS-specific dependencies), and perform a deep-dive security audit using Snyk. Stick around until the end to learn how to integrate MiniMax M2.1 into VS Code using OpenRouter.

Cybersecurity is no longer just an IT problem. It’s an Engineering problem. In his second prediction for the year, Ev argues that the traditional IT model is shifting, and the role of engineers is expanding. Traditionally, securing infrastructure belonged to the IT department. The rise of AI identities adds a layer of complexity that traditional IT tools just weren't built for. As Ev explains in this clip, if you want to protect your system from misbehaving AI, you need the people who built the system in the first place.

GitGuardian is excited to introduce Machine Learning Powered Similar Incident Grouping, which cuts through the noise by identifying incident-specific patterns across your inventory and clustering incidents that belong together, so you can handle repetitive cases efficiently and reduce incident response toil.

The GitGuardian Platform now automatically ranks every secrets incident with a risk score from 0–100, turning alert floods into a prioritized, trustworthy work queue. Scores are computed from incident context (like validity, exposure, where it was found, and exploitability) and build on existing ML capabilities like Secret Enricher and our False-Positive Remover, which cuts false positives by 80%+.