That's it, Claude is going to take down another industry again This time it's network security Anthropic tweeted an hour ago, announcing Claude Code Security Cybersecurity stocks such as Panw and CRWD fell in response It seems that the cybersecurity sector is also about to face the major problems that SaaS has just faced: Will AI modeling companies compete head-on with traditional cybersecurity giants? Will cybersecurity companies be eliminated by AI? First, let's look at the short-term reality. The ability of Claude Code Security directly impacts the field of Application Security (AppSec), especially tool based companies such as code scanning, vulnerability detection, and SAST/DAST. Because big models can understand the logical path of code, not just do rule matching. This does indeed create overlap at the capability level, which belongs to direct competition. But for comprehensive security platforms like Palo Alto Networks, CrowdStrike, and Zscaler, there is currently no direct impact. Their core lies in terminal sensors, network traffic control, cloud access channels, and real-time threat response, all of which belong to the underlying data entry and execution layer, rather than simply "cognitive analysis". The key issue is whether the model company will continue to expand along the capability boundary. Companies like OpenAI and Anthropic are essentially doing three things: understanding complex systems, identifying anomalous patterns, and generating repair strategies. These three things are precisely the core capabilities of a security platform. From the perspective of cognitive structure, there is a high degree of overlap. Therefore, long-term boundaries will inevitably meet. And the big model company has a massive user base. But the logic of 'user quantity advantage=secure data advantage' only holds half. Massive developer interaction data, API usage data, and prompt mode data are helpful for code security and application layer security. But the truly high-value security data is internal enterprise logs, terminal behavior, network traffic, and intranet topology. These data are in the hands of security vendors, and companies will not easily let model companies obtain them. That is to say, model companies have a "cognitive advantage", while security companies have a "scenario data entry advantage". But if there is a so-called AI OS in the future - an enterprise level agent operating system - the situation will be even more complex. Assuming that all systems, logs, and permission management within the enterprise are scheduled through a unified agent, security will no longer be a layer of external protection, but an embedded cognitive system. Whoever controls this AI OS has the decision-making center for anomaly detection, permission control, and automatic response. In theory, AI OS has the potential to become a secure platform. But the constraints of reality are very heavy. To become a true security platform, it is necessary to delve into the terminal kernel, network traffic, and identity system, meet compliance certification, and model companies are difficult to replace in the short term. A more realistic path may be hierarchical fusion: AI becomes the "brain" of security, while security companies still control the "nervous system and muscles". The model is responsible for judgment and strategy generation, while the security platform is responsible for data collection and execution response. The pattern for the next few years is likely not replacement, but integration. The model company becomes the cognitive infrastructure, while the security company continues to control the data entry and execution layers. Therefore, in the field of cybersecurity, the short-term is local competition, the medium-term is capability integration, and only in the long term can platform level restructuring occur. The winner is not determined by who has more users, but by who controls the core data flow and execution power of the enterprise.