Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI agents are moving from experiments into production workflows, and the Model Context Protocol (MCP) is becoming the connective layer that enables those agents to access enterprise data, applications, APIs, repositories, and automation tools. That makes MCP powerful, but also security-critical. As organizations adopt agentic AI, they need to understand not only how MCP improves connectivity but also how it creates new visibility, governance, and attack-surface challenges.

Today, Cato Networks announced a collaboration with CrowdStrike to integrate the Cato SASE Platform with the CrowdStrike Falcon platform. Together, the companies are helping security teams unify network and endpoint visibility, streamline investigations, and accelerate threat detection and response. If there’s one frustration that unites IT and security professionals, it’s this: too many tools that don’t talk to each other.

Let’s catch up on the more interesting vulnerability disclosures and cyber security news gathered from articles across the web this week. This is what we have been reading about on our coffee break! If you’re reading this and not already patched your WordPress, you might well have been breached…

Every conversation our team has with security leaders begins the same way. Nobody is backing off on AI in the SOC. The direction of the lean is what’s shifting. Torq’s 2026 AI SOC Leadership Report surveyed more than 450 CISOs and SOC leaders. The data confirms what those conversations were already telling me. We’ve left the adoption phase. The market is now in the architecture phase and the implications for how teams plan, buy, and build are significant.

The final part of CYJAX's rail cybersecurity series sets out a practical, five-step roadmap for building a rail-specific threat intelligence capability, covering regulation, coverage, and where to start regardless of maturity.

Most DLP rollouts stall in the same place. The classifier flags a file as "confidential," but nobody, including the DLP solution itself, can say why, where it came from, or whether that label still matches what's inside the file six months later. Data discovery and data classification get bundled together in nearly every vendor pitch, but they solve different problems, and the gap between them is where false positives, stale labels, and missed exfiltration events live.