Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

It’s Monday morning. Your coding agents ran all weekend. Your security dashboard shows the exact same numbers it did Friday afternoon. Same models, the same approved Model Context Protocol (MCP) servers, the same AI assets you are familiar with. Reassuring. Then, suddenly, you get a notification: a production deploy failed an audit. The build references a model nobody on your team registered.

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo The AI SOC category just got its definitive race assessment, and Torq is at the front. In the May 2026 Gartner report AI Vendor Race: Torq Is the Company to Beat in AI SOC Agents for Threat Investigation (Document ID: G00855833), Gartner names Torq the Company to Beat.

For much of security history, one metric dominated: recall. Recall means: of all the sensitive data that exists, how much did you catch? If there are 100 pieces of PII in a document and your system finds 95, your recall is 95 percent. This made sense in the old security world. If a firewall missed a real threat, the company had a serious problem. If it blocked something safe, someone could investigate and fix it.

At the World Economic Forum cyber meeting in Geneva recently, I had an interesting conversation with Vinh Nguyen, who is a strategic security advisor and Senior Fellow for AI at CFR. I wanted to know from him how he sees runtime governance in agentic AI working out practically and what approaches actually work. One of the challenges he mentioned was that yes, we need runtime governance to provide continuous and real time assurance that agents are doing what they are supposed to be doing.