Introduction: As AI evolves from 'smarter' to 'autonomous,' security has shifted from a technical option to a commercial necessity.

Over the past two years, the artificial intelligence industry has faced one overriding question: How can AI be made stronger?

The capital market has built a vast industrial chain around this goal.

NVIDIA GPUs have become one of the most closely watched assets in the global capital market, with servers, optical modules, liquid cooling, power, and data centers emerging as the hottest investment areas in the AI era.

However, a new change is taking place in 2026.

The AI industry is now confronting another, more pressing question: As models gain increasingly strong reasoning, coding, and autonomous execution capabilities, how can humans ensure they remain controllable?

Previously, OpenAI adjusted the development pace of some advanced models, drawing market attention.

This did not mean a halt in AI development but rather a reassessment of safety evaluations, model monitoring, and risk control systems as model capabilities improved.

From 'pursuing stronger intelligence' to 'ensuring intelligent security,' the AI industry is entering a new phase.

01 The Logic of Large Model Competition Has Completely Changed

The development logic of the AI industry over the past few years has been straightforward: larger models, more data, greater computing power, and faster iteration.

From GPT-series models to products like Gemini and Claude, global tech companies have continuously pushed the boundaries of large model capabilities.

However, as model capabilities improve, a new issue arises: AI is evolving from a 'tool for answering questions' to an 'intelligent agent capable of executing tasks.'

In the past, users posed questions, and AI generated answers; in the future, users will set goals, and AI will autonomously plan steps and use tools to complete tasks.

With enhanced capabilities comes an expanded risk boundary.

If an ordinary chatbot makes a mistake, the impact may be limited to a single incorrect response. However, if an AI Agent connected to a corporate system errs, the consequences could involve customer data, trade secrets, internal processes, or even critical infrastructure.

Thus, AI security is transitioning from a 'research issue' to a 'commercial issue.'

Global AI companies have begun adjusting their security strategies.

OpenAI has publicly stated that during the development of advanced models, the company continuously conducts safety assessments, including model capability testing, risk evaluation, and deployment restrictions. For models with stronger coding and cybersecurity capabilities, additional assessments of potential risks are required.

This indicates a shift in the evaluation criteria for large model competition.

02 The Fatal Allure of AI Agents

The focus on AI security arises not because the industry has suddenly changed direction but because more cases show that AI risks are being exposed as applications expand.

Traditional software systems primarily operate under fixed rules: programmers write the logic, and the system executes commands.

However, AI Agents differ—they can understand goals and autonomously decide the next action.

This presents new security challenges, such as: What if the goal is misunderstood? What if permissions are excessive? What if the system is maliciously induced?

This is why issues like 'prompt injection attacks,' 'model privilege escalation,' and 'data leaks' have become critical topics in AI security in recent years.

For instance, when deploying AI assistants, companies often want them to access more internal information. However, greater access comes with higher risks. An AI assistant that helps employees improve efficiency could also become a new entry point for data breaches.

This creates a unique contradiction in the AI era: companies want AI to be stronger but must also restrict it.

Similar issues have arisen during testing by overseas AI companies.

Google has disclosed that in AI security research, it tests whether models can break out of restricted environments through simulated attacks.

The purpose of such tests is not to prove that models are out of control but to identify potential risks in advance.

Companies like Anthropic also continuously conduct model safety assessments, including testing cybersecurity capabilities, long-task execution abilities, and model behavior.

These cases demonstrate that AI security has become an unavoidable part of the commercialization of large models.

At the same time, AI is transforming cyberattack methods.

In the past, hacking required extensive manual analysis; in the future, attackers may use AI to automatically find vulnerabilities, generate attack code, and analyze target systems.

This means AI could become both a security tool and a new attack tool. The security industry is entering an era of 'AI versus AI.'

03 The Next Battle in AI Will Be Over 'Security'

Looking back at past cycles of technological development, a pattern emerges: every technological revolution brings new security demands.

For example, the expansion of connectivity in the internet era spurred cybersecurity; the migration of enterprises to the cloud in the cloud computing era drove cloud security; and in the AI era, the integration of intelligent systems into production environments is fueling AI security.

Over the past two years, capital markets have focused on AI infrastructure. Since model training requires massive computing power, GPU prices rose, server production expanded, and data center construction accelerated.

However, as AI enters the enterprise application phase, new bottlenecks are emerging: Do companies dare to use AI? Can they trust AI with their core operations?

For enterprises, AI adoption is not just a technical issue but also a risk management one. For example, a bank deploying an intelligent customer service system must ensure customer information is secure; a manufacturing company deploying an AI production assistant must prevent production data leaks; and a company deploying an AI office system must guard against the misuse of internal materials by the model.

This is why overseas markets are refocusing on AI security.

Cybersecurity companies are integrating AI capabilities into their products while developing new protection solutions for AI systems. For example, enterprises like Palo Alto Networks and CrowdStrike are exploring AI-driven security operations, threat detection, and enterprise AI environment protection.

Their development paths show that the security industry's real growth opportunities often arise from changes in technological infrastructure.

04 AI Security Won't Create Another NVIDIA

If the greatest opportunity in the AI industry over the past two years has been computing power, then security may become an indispensable part of AI commercialization in the next phase.

However, unlike GPUs or servers, AI security will not emerge as a single, dominant sector. Instead, it will resemble the cybersecurity industry of the past two decades, with decentralized demand across various scenarios but expanding alongside the broader digital industry.

The same is true in the AI era.

As large models gradually enter fields like office work, finance, manufacturing, government services, and energy, companies' concerns have shifted from 'whether AI capabilities exist' to 'how to safely integrate AI into production systems.'

This means traditional security companies are facing new industrial opportunities.

In the past, corporate security efforts primarily focused on preventing external attacks from infiltrating systems.

Firewalls, antivirus software, vulnerability detection, and security operation platforms formed the traditional cybersecurity framework.

However, in the AI era, security boundaries are changing. Companies must not only prevent external attacks but also ensure that AI does not access unauthorized data, perform prohibited actions, or generate non-compliant content.

In short, security used to protect 'systems'; in the future, it will protect 'intelligence.'

In this shift, domestic cybersecurity companies are seeking new growth opportunities.

Take Qi An Xin as an example. The company has long served clients in critical industries like government, finance, and energy, with business covering cybersecurity, data security, and cloud security.

Previously, clients' core demand for security products was to protect networks and business systems. However, as companies deploy large models, security needs are evolving in new directions.

For instance, after a company launches an internal AI assistant, it must address questions like: Does employee-inputted data contain sensitive information? Will the model leak internal materials? Are the AI's permissions when accessing corporate systems excessive?

These issues essentially exceed the scope of traditional network protection. Therefore, for traditional security vendors, AI security is not an entirely new market but an extension of existing security capabilities into AI scenarios.

Similar changes are occurring at Venustech.

For years, Venustech has primarily served government and enterprise clients, accumulating extensive experience in cybersecurity and information security operations. Industries like government, power, and finance are also among the earliest adopters of AI applications.

The reason is that these sectors possess vast data resources and have higher security requirements.

For example, when financial institutions use AI for intelligent customer service, investment research support, and risk analysis, they must simultaneously meet efficiency, data security, and compliance requirements. This means that as AI applications scale, the demand for security operations capabilities will also rise.

In the cloud computing and enterprise digitization sectors, AI security is becoming a new demand.

Sangfor has long focused on enterprise-grade IT infrastructure, cloud computing, and cybersecurity. Over the past few years, enterprise digitization has driven growth in cloud security demand. In the AI era, corporate IT architectures are becoming even more complex.

A company may soon simultaneously operate cloud servers, enterprise databases, knowledge bases, AI Agents, and automated business processes.

The tighter the connections between these systems, the higher the difficulty of security management. Therefore, AI security is not an isolated new business but an integrated upgrade of cloud security, data security, and network security.

Beyond traditional cybersecurity vendors, the data security sector may also become a critical direction for AI security.

One of the greatest values of large models is their ability to understand corporate data. However, data is also a company's most sensitive asset.

Companies want AI to know more but must ensure it 'does not know what it shouldn't.' This creates a new security contradiction in the AI era: Which data can be opened to models? Which data must be isolated? Does the model record sensitive information? Does AI-generated content comply with regulatory requirements?

These questions require new security technologies to address. Therefore, security companies like DBAPPSecurity, Meiya Pico, and NSFOCUS are also focusing on data security and security detection.

From a capital market perspective, the real focus of AI security is not short-term order growth for individual companies but the changing industrial logic.

In the past, AI drove companies to purchase more computing power; in the future, large-scale AI adoption may drive companies to purchase more security capabilities because they are willing to adopt a smarter AI—but only if it is trustworthy.

05 AI Is Repeating the Path of Cloud Computing

Looking back at the development of the internet industry, a clear pattern emerges: In the early stages of technological revolutions, the market focuses on efficiency gains. However, when technology enters large-scale commercial application, security often becomes the new infrastructure.

This was true in the internet era, the cloud computing era, and may well be true in the AI era.

Two decades ago, the internet rapidly expanded. Companies' primary concern was how to connect more users and establish online businesses.

However, as the internet scaled, viruses, hacking, and data breaches became frequent. This spurred rapid growth in the cybersecurity industry, with companies investing in firewalls, endpoint security, and security operations.

Subsequently, cloud computing became the new infrastructure. As companies migrated servers and business systems to the cloud, security issues resurfaced, making cloud security a new growth area.

In overseas markets, companies like CrowdStrike and Palo Alto Networks emerged as new-generation security firms alongside enterprise digital transformation.

Their growth logic was not driven by a single security incident but by the increasing reliance of companies on digital systems, which elevated the value of security.

AI may be following a similar path.

In the past, discussions around AI focused on model capabilities, costs, and efficiency. However, as AI begins to enter core business processes, companies must now ask: What if AI makes a mistake? What if AI leaks data? What if AI is attacked?

This means that in the future, corporate AI deployments may form a 'three-piece' infrastructure: large models for capability, computing power for operations, and security systems for trustworthiness.

Without a security framework, the stronger a large model becomes, the more cautious companies will be. After all, no company will entrust its core operations to an uncontrollable system.

Of course, AI security is still in its early stages. The market currently faces several challenges:

First, technical standards are not yet fully formed, and different companies have varying understandings of AI risks.

Second, business models are still being explored, and it remains to be seen whether companies are willing to pay separately for AI security.

Third, the competitive landscape is not yet stable, with traditional security firms, cloud providers, and large model companies all potentially entering the field.

However, the long-term trend is clear: artificial intelligence is transforming corporate IT architectures. Every change in IT architecture redefines the security industry.

06 Conclusion

In the first phase of the AI industry over the past two years, the focus was on capability competition.

Whoever had stronger models, more computing power, and could make machines smarter held the advantage.

However, as AI begins to enter the real world, the competitive logic is changing. In the future, the contest will not just be about who can create stronger AI but also about who can make AI trustworthy enough for widespread use.

OpenAI's adjustment of the development pace for some advanced models does not signal the end of the AI boom. Instead, it indicates that artificial intelligence is entering a more mature phase.

In the first phase, humanity's question was how to make AI more capable.

In the second phase, the question is how to keep powerful AI controllable.

In the internet era, security enabled connectivity.

In the cloud computing era, security enabled migration.

In the artificial intelligence era, security may enable the integration of intelligence into the real world.

Thus, AI security will not merely be a subsidiary part of the AI industry. It could become the new infrastructure for the next wave of the AI revolution.

And the capital market may no longer just seek companies building 'brains' but also those ensuring those 'brains operate safely.'