Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo If you are evaluating security platforms in 2026 based on which one has the best chatbot or can write a slightly better Python script for you, you’re fighting the last war. Attackers are already using AI to scale their operations with speed and precision. If your “AI SOC platform” is just a co-pilot that summarizes tickets while humans do all the work, you’re behind.

LLM red teaming is a proactive security practice that involves systematically testing large language models (LLMs) with adversarial inputs to find vulnerabilities before deployment. By using manual or automated methods to probe for weaknesses, red teamers can identify issues like harmful content generation, bias, or security exploits, which are then addressed through a continuous “break-fix” loop to improve the model’s safety and reliability.

AI-generated phishing emails now achieve a 54% click-through rate against just 12% for human-crafted messages. No, that is not a typo! With AI, attackers are now 4.5x more effective at breaching and bleeding your defences. Secondly, phishing attacks have surged by over 1,265% since ChatGPT’s launch in 2022, enabling cybercriminals to launch campaigns at unprecedented scales. The harsh reality?

Over the last year, Model Context Protocol (MCP) servers have transitioned from "cool developer experiments" into critical production infrastructure. Developers love them because they allow AI agents to open tickets, query databases, and update records with almost zero integration backlog. But there is a fundamental truth we must acknowledge before moving forward: The AI revolution is actually an API revolution.

MITRE ATLAS has become a critical resource for cybersecurity leaders navigating the rapidly evolving world of AI-enabled systems.Traditional threat models are built for human-initiated workflows, APIs, and infrastructure, so they are no longer sufficient to describe modern AI attacks..