Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

“AI-powered DAST” is everywhere. It signals progress, but assumes something fundamental was missing. It wasn’t. DAST struggled not from lack of intelligence, but from lack of depth. Most tools never reached inside authenticated, stateful, multi-step journeys where real logic, sensitive data, and critical vulnerabilities exist. That’s the part Appknox solved years ago. AI here is not a reset. It is an accelerator, applied to a system already operating where risk actually lives.

Agentic AI workloads are shipping to production on Kubernetes faster than the standards to secure them. Many teams deploying autonomous, tool-calling agents as containerized microservices do so without a shared baseline for securing or monitoring those containers. The CNCF AI Technical Community Group recently published a comprehensive article on cloud-native agentic standards, marking the first attempt to define best practices for such deployments.

Security budgets are tightening, and tool consolidation reviews keep landing on the same three categories: data security posture management (DSPM), data loss prevention (DLP), and AI security. At the same time, vendor marketing has done little to clarify the differences among the three and the path for organizations needing to enhance data security efficiently.

In the fourth installment of SafeBreach’s AI-First evolution series, VP of Development Yossi Attas and Principal Software Design Engineer Guy Ephraim explore how test-driven development (TDD) serves as the essential “safety net” for high-speed AI code generation.

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo I recently sat between two people who think about the AI SOC operations from completely different angles — and spent 50 minutes watching them land in the same place.

I am no stranger to conferences, and certainly no stranger to security conferences. Over the years, BlackHat and DEFCON have both become staples of my calendar. But this year brought a new one to the list: RSA, and it truly lived up to the hype. The show floor was full of bright lights, fancy booths, and yes, tattoos, if you knew where to find them.

When people talk about AI security risks, the conversation usually starts with the model. Can it be jailbroken? Can someone get around the guardrails? Can an attacker make it say or do something it should not? Those are fair questions, but they are not the most important ones. The bigger risk is not the model on its own: it’s everything the model is connected to.

Source code for Claude Code, Anthropic’s developer facing CLI tool, was exposed through source maps included in a published npm package under Anthropic’s npm namespace. The exposure revealed approximately 500,000 lines of application code across roughly 1,900 files.