Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A nasty shock is hitting finance leaders across every industry right now: AI token bills that run ten, twenty, even a hundred times over what they forecasted, blowing holes straight through quarterly budgets. These leaders are all asking the same questions: How could this happen if they didn't approve it? Why didn't any of their systems alert them to the spike? And most importantly, what can they do now?

An AI agent on a developer's laptop has read access to a code repository, a set of internal documents, and an external model. Nobody approved that specific combination, and nobody is watching what the agent does with it session to session. The agent is not malicious, however, it is doing exactly what it was configured to do. But, that configuration is the exposure, and most security teams do not have a way to see it, let alone stop it before sensitive data leaves the environment.

When Anthropic revealed Claude Mythos and Project Glasswing, the industry did what the industry always does with a frontier-AI story: it reached for the alarm. The headlines, Reddit threads, and back-channel conversations all focused on the same things: All of that is real, and none of it is the part that should keep a security leader up at night. Here is the part that should.

Quantum computers capable of breaking today’s encryption may still be years away, but France’s National Cybersecurity Agency (ANSSI) believes organisations shouldn’t wait to prepare. At the France Quantum Conference on June 16, 2026, ANSSI announced new milestones for the adoption of Post-Quantum Cryptography (PQC). ANSSI recommends that organisations prioritise purchasing quantum-safe security products by 2030. The most important message, however, isn’t about 2030.

The discussion during our recent webinar made one thing clear. Security teams aren't asking whether coding agents will become part of the enterprise. They're asking how to adopt them safely. The audience questions focused on practical concerns that many organizations are facing today, from autonomous execution to supply chain risk and governance. Here are the five questions that generated the most discussion.

Model Context Protocol (MCP) is what turns an AI assistant into an AI agent. It’s the standardized bridge that lets models call real tools – read files, query databases, send messages, pull emails. That capability is the whole point. It’s also what makes MCP environments a target. Most deployments were scoped for what the agent needed to do. Not for what happens when that access is turned against the organization.