On Stocktwits, retail sentiment around Nvidia continued to trend in \u2018extremely bullish\u2019 territory over the past day, while sentiment around CrowdStrike dipped to \u2018bullish\u2019 from the \u2018extremely bullish\u2019 zone.<\/p>\n
Huang Sees AI Creating New Cybersecurity Arms Race<\/p>\n
\u201cI believe in closed models and open models,\u201d Huang stated at Fal.Con. \u201dWe should use closed models and proprietary models and off-the-shelf as much as you can. You know why build something unless you have to.\u201d<\/p>\n
However, he added that the cybersecurity industry is moving toward a contest between autonomous AI systems on both sides of an attack.<\/p>\n
\u201cAttacks are happening at inference speed,\u201d he said, adding that traditional measures such as breakout time are becoming less relevant as AI agents can perform multiple actions without direct human intervention.<\/p>\n
\u201cWe don’t need every AI to be super smart at everything,\u201d Huang said. \u201cBut in some areas we need to be extraordinarily good at something.\u201d According to him, cyber defense is one of those areas.<\/p>\n
Huang stated Nvidia’s Nemotron models were designed to be customizable and cost-effective for specialized applications such as cybersecurity. He added that CrowdStrike can fine-tune the models using its cybersecurity expertise and data, creating an AI system specifically optimized for detecting and responding to threats.<\/p>\n
Nvidia, CrowdStrike Build SafeMind Cyber Defense System<\/p>\n
CrowdStrike CEO George Kurtz stated that SafeMind was cybersecurity\u2019s \u201cfirst complete agentic system\u201d for cybersecurity, combining AI models with a specialized software \u201charness\u201d within the platform.<\/p>\n
SafeMind pairs two models, the first of which is Blue Solano, designed for defensive cybersecurity. The second, called Red Tempest, is designed to simulate sophisticated attacks. The models operate against each other in a digital twin of an enterprise environment, allowing the defensive system to learn from simulated attacks and continuously improve its ability to detect and respond to threats.<\/p>\n
Huang said the approach could eventually extend beyond enterprise cybersecurity. He pointed to robotics, factories, edge computing and other physical AI applications as areas where the same framework could be deployed.\u00a0<\/p>\n
Read also:\u00a0Nvidia Puts $3B Into SB Energy, OpenAI Holds Nearly 4M Warrants In IPO<\/a><\/p>\n For updates and corrections, email newsroom[at]stocktwits[dot]com.<\/p>\n
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