Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

According to new research from Cyberhaven Labs, drawn from real-world data movement events and active user counts across Cyberhaven's enterprise customer base between December 2025 and June 2026, Claude Desktop usage grew 1,233% between January and June 2026.

“The call is coming from inside the house.” It’s one of horror’s oldest lines, and you already know how the scene goes. The team scrambles, rechecks every firewall, audits every login, hunting for an intruder. Then the trace comes back, and there isn’t one because there is no malware or forced entry. Just an employee, at their own desk, with their own login, who pasted a confidential spreadsheet into an unapproved AI tool to save 10 minutes before a deadline.

AI is compressing the cybersecurity timeline faster than most institutions can adapt to it. Not only do security leaders have to deal with this new reality, they have to answer key questions to internal and external audiences about their efforts — including regulators.

Coding agents have a misconfiguration problem. YOLO mode enabled to reduce approval friction, sandbox enforcement disabled, Model Context Protocol (MCP) servers installed from public marketplaces without security review. These are common configurations in enterprise developer environments, and remediating them is genuinely valuable work. AI Security Posture Management (AISPM) addresses exactly this class of risk, surfacing misconfigurations before they create the conditions for a successful attack.

A user asks an enterprise AI assistant a normal question: “Why did we lose the Acme deal?” The agent does what agents do. It retrieves CRM notes, pricing history, discount approvals, sales leadership comments, and a couple of internal strategy docs, then combines them into one clear answer: “Acme received a 28% discount exception, well above our standard enterprise pricing.

New GenAI code security research shows a stubborn truth: as AI is generating more of the code entering production, secure output is not improving at the same pace. The result is a GenAI code security challenge defined by scale, model choice, and the growing need for verification. AI coding has moved past experimentation. For many teams, it is now part of how software gets built every day. That’s the opportunity. It’s also the risk.