Introduction: As AI evolves from being "smarter" to "autonomous," security has shifted from a technical option to a commercial imperative.
Over the past two years, there has been only one most critical question in the AI industry: how to make AI more powerful.
Capital markets have built a massive industrial chain around this goal.
NVIDIA GPUs have become one of the most watched assets in global capital markets, with servers, optical modules, liquid cooling, power, and data centers emerging as the hottest investment directions in the AI era.
However, as we enter 2026, a new shift is taking place.
The AI industry is beginning to face another more practical question: as models possess increasingly strong reasoning, coding, and autonomous execution capabilities, how can humans ensure they remain within a controllable range?
Previously, OpenAI adjusted the R&D pace of some advanced models, sparking market attention.
This does not mean AI R&D has stopped; rather, as model capabilities improve, the company is re-evaluating its security assessment, model monitoring, and risk control systems.
From "pursuing stronger intelligence" to "ensuring intelligent security," the AI industry is entering a new phase.
The Logic of Large Model Competition Has Completely Changed
The development logic of the AI industry over the past few years has been very simple: larger models, more data, more computing power, and faster iterations.
From the GPT series models to products like Gemini and Claude, global tech companies are continuously pushing the boundaries of large model capabilities.
However, as model capabilities improve, a new issue is gradually emerging: AI is transforming from a "tool for answering questions" into an "agent capable of executing tasks."
In the past, users asked questions and AI generated answers; in the future, users will set goals, and AI will autonomously plan steps and invoke tools to complete tasks.
The flip side of capability enhancement is that the risk boundaries are also expanding.
If an ordinary chatbot makes an error, the impact might just be a wrong answer, but if an AI Agent connected to enterprise systems makes an error, the impact could involve customer data, trade secrets, internal processes, or even critical infrastructure.
Therefore, AI security is shifting from a "research issue" to a "business issue."
Global AI companies have already begun to adjust their security strategies.
OpenAI has publicly stated that during the development of advanced models, the company will continuously conduct security assessments, including model capability testing, risk assessment, and deployment restrictions. For models with stronger coding and cybersecurity capabilities, enterprises need to conduct additional evaluations of their potential risks.
This means that the evaluation criteria for large model competition are changing.
The Fatal Temptation of AI Agents
The reason AI security is receiving attention is not because the industry has suddenly changed direction, but because an increasing number of cases show that AI risks are being exposed as applications expand.
Past software systems mainly operated according to fixed rules: programmers wrote the logic, and the system executed the commands.
But AI Agents are different; they can understand goals and autonomously decide on the next actions.
This brings new security challenges: what if the goal is misunderstood? What if permissions are too high? What if it is maliciously manipulated?
This is also why issues such as "prompt injection attacks," "model privilege escalation," and "data leaks" have become important topics in the field of AI security in recent years.
For example, when deploying AI assistants, enterprises often want them to access more internal information. But the greater the access permissions, the higher the risk. An AI assistant that can help employees improve efficiency could simultaneously become a new data entry point.
This forms a unique contradiction in the AI era: enterprises want AI to be stronger, but they must also restrict it.
Similar issues have also emerged during testing by overseas AI companies.
Google has disclosed that in AI security research, it tests whether models might break through restricted environments through simulated attacks.
The purpose of such tests is not to prove that the model is out of control, but to identify potential risks in advance.
Companies like Anthropic are also continuously conducting model security assessments, including testing cybersecurity capabilities, long-task execution capabilities, and model behavior.
These cases illustrate that AI security has become an unavoidable link in the commercialization process of large models.
At the same time, AI is also changing the methods of cyberattacks.
In the past, hacker attacks required a lot of manual analysis; in the future, attackers may use AI to automatically find vulnerabilities, generate attack code, and analyze target systems.
This means that AI can be both a security tool and a new attack tool. The security industry is entering a phase of "AI versus AI."
The Next War in AI is About "Security"
Looking back at past rounds of tech industry development, a pattern can be found: every technological revolution is accompanied by new security demands.
For instance, in the Internet era, the expansion of connection scale gave birth to cybersecurity; in the cloud computing era, enterprise migration to the cloud gave birth to cloud security; and in the AI era, the entry of intelligent systems into production environments is giving birth to AI security.
Over the past two years, capital markets have focused on AI infrastructure. Because model training requires massive computing power, GPU demand surged, server production expanded, and data center construction accelerated.
But as AI enters the enterprise application stage, new bottlenecks are emerging: do enterprises dare to use AI? Can they confidently hand over core businesses to AI?
For enterprises, AI implementation is not only a technical issue but also a risk management issue. For example, a bank deploying an intelligent customer service system needs to consider whether customer information is secure; a manufacturing enterprise deploying an AI production assistant needs to consider whether production data might be leaked; an enterprise deploying an AI office system needs to consider whether internal materials might be mistakenly invoked by the model.
This is also why overseas markets are refocusing on AI security.
Cybersecurity companies are beginning to integrate AI capabilities into security products while developing new protection solutions specifically for AI systems. For example, companies like Palo Alto Networks and CrowdStrike are exploring AI-driven security operations, threat detection, and the protection of enterprise AI environments.
Their development paths indicate that the real growth opportunities in the security industry often come from changes in technological infrastructure.
AI Security Will Not Create Another NVIDIA
If the biggest opportunity in the AI industry over the past two years came from computing power, then in the next phase, security may become an indispensable link in the commercialization process of AI.
But unlike GPUs and servers, AI security will not give birth to a single massive market. It is more like the cybersecurity industry over the past two decades, where demands are scattered across different scenarios but continue to expand alongside the development of the entire digital industry.
The same applies to the AI era.
As large models gradually enter fields such as office work, finance, manufacturing, government affairs, and energy, the problems enterprises face have shifted from "whether they have AI capabilities" to "how to safely integrate AI into production systems."
This also means that traditional security enterprises are ushering in new industrial opportunities.
In the past, enterprise security construction mainly focused on solving how to prevent external attacks from entering the system.
Firewalls, antivirus software, vulnerability detection, and security operations platforms constitute the traditional cybersecurity system.
But in the AI era, security boundaries are changing. Enterprises not only need to prevent others from attacking their systems but also need to prevent AI from accessing data it shouldn't, executing operations it shouldn't, and generating non-compliant content.
Simply put, in the past, security protected "systems"; in the future, security will protect "intelligence."
Amidst this change, domestic cybersecurity enterprises are seeking new growth spaces.
Taking QiAnXin as an example, the company has long served key industry clients in government, finance, energy, etc., with its business covering cybersecurity, data security, cloud security, and other fields.
In the past, the core demand for customers purchasing security products was to protect networks and business systems. But as enterprises deploy large models, security demands are extending in new directions.
For example, after an enterprise launches an internal AI assistant, it needs to address whether the data input by employees contains sensitive information, whether the model will leak internal materials, and whether the permissions are too excessive when AI invokes enterprise systems.
These issues have essentially gone beyond the scope of traditional network protection. Therefore, for traditional security vendors, AI security is not a completely unfamiliar new market, but an extension of existing security capabilities into AI scenarios.
Similar changes are also happening to Venustech.
For a long time, Venustech has mainly served government and enterprise clients, accumulating extensive experience in cybersecurity and information security operations. And industries such as government, power, and finance are precisely the areas where AI applications will be implemented earliest in the future.
The reason is that these industries possess massive data resources and simultaneously have higher security requirements.
For example, when financial institutions use AI for intelligent customer service, investment research assistance, and risk analysis, they need to simultaneously meet the requirements for efficiency improvement, data security, and compliance. This means that the larger the scale of AI applications in the future, the higher the requirements for security operations capabilities.
In the fields of cloud computing and enterprise digitalization, AI security is also becoming a new demand.
Sangfor has long been deploying itself in enterprise-level IT infrastructure, cloud computing, and cybersecurity. Over the past few years, enterprise digital transformation has driven the growth of cloud security demand. In the AI era, enterprise IT architectures are becoming further complicated.
In the future, an enterprise may simultaneously possess cloud servers, enterprise databases, knowledge bases, AI Agents, and automated business processes.
The more closely these systems are connected, the higher the difficulty of security management. Therefore, AI security is not an isolated new business, but a converged upgrade of cloud security, data security, and cybersecurity.
Besides traditional cybersecurity vendors, the data security field may also become an important direction for AI security.
One of the greatest values of large models is their ability to understand enterprise data. But at the same time, data is also the most sensitive asset of an enterprise.
Enterprises want AI to know more, but they must also ensure that AI "does not know what it shouldn't know." This forms a new security contradiction in the AI era: which data can be opened to the model? Which data must be isolated? Does the model record sensitive information? Does the AI-generated content comply with regulatory requirements?
These issues all require new security technologies to solve. Therefore, security enterprises including DBAPPSecurity, Meiya Pico, and NSFOCUS are also deploying around data security and security detection directions.
From the perspective of capital markets, what is truly worth paying attention to in AI security is not the short-term order growth of a certain company, but the changing industrial logic.
In the past, AI drove enterprises to purchase more computing power; in the future, the scaled application of AI may drive enterprises to purchase more security capabilities, because enterprises are willing to buy a smarter AI, provided that it must be trustworthy.
AI is Repeating the Old Path Taken by Cloud Computing
Looking back at the development of the Internet industry, an obvious pattern can be found: in the early stages of a technological revolution, the market focuses on efficiency improvement. But when the technology enters the stage of large-scale commercial application, security often becomes the new infrastructure.
This was true in the Internet era, true in the cloud computing era, and likely true in the AI era as well.
Twenty years ago, the Internet was rapidly popularized. The most concerning issues for enterprises were: how to connect more users and how to establish online businesses.
But as the scale of the Internet expanded, viruses, hacker attacks, and data leaks began to appear frequently. As a result, the cybersecurity industry developed rapidly, and enterprises began to invest in firewalls, endpoint security, and security operations.
Subsequently, cloud computing became the new infrastructure. Enterprises migrated servers and business systems to the cloud, and security issues emerged once again. Thus, cloud security became a new growth direction.
In overseas markets, companies like CrowdStrike and Palo Alto Networks are the new generation of security enterprises that have grown alongside enterprise digital transformation.
Their development logic does not rely on a single security incident, but because enterprises are increasingly dependent on digital systems, and therefore, the value of security is getting higher and higher.
AI may be repeating a similar path.
In the past, when discussing AI, enterprises focused on model capabilities, costs, and efficiency. But as AI begins to enter core business processes, enterprises must answer: what if AI makes an error? What if AI leaks data? What if AI is attacked?
This means that in the future, enterprise deployment of AI may form an infrastructure similar to a "three-piece set": large models solve the capability issue, computing power solves the operation issue, and the security system solves the trustworthiness issue.
Without a security system, the stronger the large model's capabilities, the more cautious enterprises will be. Because enterprises will not hand over core businesses to an uncontrollable system.
Of course, AI security is still in its early stages. The market currently still faces several issues:
First, technical standards have not yet been fully formed, and different enterprises have different understandings of AI risks.
Second, the business model is still being explored, and it will take time to verify whether enterprises are willing to pay separately for AI security.
Third, the industry competition landscape has not yet stabilized; traditional security companies, cloud vendors, and large model companies may all enter this field.
But the long-term trend has already emerged: AI is changing enterprise IT architectures. And every change in IT architecture will redefine the security industry.
Conclusion
Over the past two years, the first phase of the AI industry has been about capability competition.
Whoever possesses stronger models, whoever possesses more computing power, and whoever can make machines smarter.
But as AI begins to enter the real world, the logic of competition is changing. In the future, the contest will not only be about who can create stronger AI, but also who can make AI safe and reassuring to use.
OpenAI's adjustment of the R&D pace for some advanced models does not mean the end of the AI boom. On the contrary, it indicates that AI is entering a more mature phase.
In the first phase, the question for humanity was how to give AI stronger capabilities.
In the second phase, the question for humanity is how to keep powerful AI controllable.
In the past Internet era, security ensured connectivity.
In the cloud computing era, security ensured migration.
In the AI era, security may ensure the possibility of intelligence entering the real world.
Therefore, AI security will not just be an ancillary link in the AI industry. It may become the new infrastructure in the next round of AI revolution.
And what capital markets are looking for may no longer be just the companies that build "brains," but also those enterprises that help "brains run safely."