Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The Prescriptive Path to Operationalizing AI Security

In introducing the AI Security Fabric, we have outlined how security must evolve as software is built by humans, models, and autonomous agents working at machine speed. The Fabric defines the architectural shift required to build trust at AI speed, delivered through the Snyk AI Security Platform. We’re now focusing on the next question: how organizations put that vision into practice. Operationalizing AI security is not about enabling a single feature or deploying a tool.

Introducing the AI Security Fabric: Empowering Software Builders in the Era of AI

Today, we’re thrilled to introduce the AI Security Fabric, delivered through the Snyk AI Security Platform, and operationalized through a prescriptive path for AI security. As software creation shifts to humans, models, and autonomous agents working together at machine speed, security must evolve just as fundamentally. The AI Security Fabric defines the new paradigm, and the Prescriptive Path shows how the Snyk AI Security Platform gets you there.

Snyk Advisor is Reshaping Package Intelligence on Snyk Security Database

Choosing safe, healthy open source dependencies shouldn’t require jumping between tools or piecing together context from multiple places. Developers and AppSec teams need package health signals exactly where security decisions already happen. This is why we’re bringing Snyk Advisor data into security.snyk.io.

GLM 4.7 vs. The Giants: Is This the New King of AI Coding?

Can a lesser-known model compete with the likes of OpenAI, Google, and Anthropic? In this video, we put Z.ai’s GLM 4.7 to the ultimate test. We task it with building a production-ready, secure Node.js note-taking application from a single prompt to see if its code quality and security stand up to the big name foundational models.