Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Defensive asymmetry. The visibility gap. The defender’s dilemma. These terms describe a significant challenge: attackers often succeed because they uncover information defenders either don’t know about or can’t act on quickly enough.

AI didn’t just make defenders faster. It made attackers faster too. The moment both sides got access to the same speed, speed stopped being the advantage. With speed no longer separating attackers from defenders, the deciding factor moved somewhere else. Ask a CISO what’s actually kept agentic security out of reach, and it comes down to this: they can’t afford to build it, and even if they could, they’d struggle to trust it. Neither half of that problem outweighs the other.

Enterprise buyers evaluating AI agent security in 2026 face a market that has fragmented into specialized categories, each solving one layer of the problem well and other layers poorly. Identity vendors govern non-human credentials. Runtime vendors constrain what agents can do at the moment of execution. Established security platforms extend their existing offerings into the agentic space. ‍

AI-related security incident detection starts with knowing what AI systems are running across the organization. Without a complete, continuously updated inventory of sanctioned, shadow, and third-party AI tools, security teams cannot detect incidents involving systems they do not know exist. From there, effective incident management requires a structured response framework that connects detection to containment, investigation, remediation, regulatory notification, and governance integration. ‍

An AI acceptable use policy (AUP) is a formal set of rules that defines how employees can safely and responsibly use AI tools in the workplace. Its purpose is to encourage AI-driven productivity while protecting the organization from data leaks, intellectual property exposure, compliance violations, and the security vulnerabilities that unsanctioned AI usage introduces. Every organization deploying or permitting AI tools needs one. ‍

Software Analyst Cyber Research (SACR) just published its 2026 AI SOC Market Report, offering an independent assessment of vendors competing in what has rapidly become the most consequential category in enterprise security. Torq features prominently, and the report’s framing is worth unpacking because it illuminates what makes the AI SOC category hard to evaluate and why Torq’s approach is consequential.