Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

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.

Organizations have invested heavily in consent management. Consent Management Platforms (CMPs) are standard infrastructure for privacy programs, and for good reason. Regulations like GDPR, CCPA/CPRA, LGPD, PDPA, and HIPAA require organizations to obtain, record, and honor user consent before collecting or processing personal data. CMPs provide the framework to do that. Most organizations have done the right thing, they just don’t know if they’ve done the right thing right.

Anthropic’s upcoming Mythos model points to something far more consequential than another leap in artificial intelligence. It signals a shift that could redefine the balance between attackers and defenders in cyberspace.

For managed security service providers (MSSPs), customer loyalty is the most critical indicator of business health. Unlike other metrics that you directly control, such as mean time to respond or mean time to detect, it can’t be gamed: customers will either stay with you or they’ll churn. This means that the top priority for any MSSP should be to deliver the specific customer outcomes they were hired to provide, like helping to stop threat actors before they cause damage.