After US AI company Anthropic experienced a series of security incidents involving unauthorized access to government systems, the Trump administration announced on October 12 the implementation of a mandatory AI security incident reporting regime. The new rules require AI companies to immediately notify the government after a security incident occurs, enhance transparency in incident handling, and implement remediation and corrective measures for affected agencies. This move marks a substantive shift in US AI security regulation from industry self-discipline to administrative mandate.
From an industry logic perspective, the scope of cybersecurity protection is extending from traditional IT architecture to large-model assets. AI Security Posture Management (AI-SPM) is no longer an optional add-on module but a rigid requirement for enterprises deploying large models. IDC forecasts that revenue from AI Agent applications related to cybersecurity in China will grow at a compound annual rate of 106.5%, with the market size expected to reach 59.35 billion yuan (approximately $8.9 billion) by 2030 — a figure nearly twice the size of the AI security market itself.
China Galaxy Securities noted in its latest research that AI security is transforming from traditional bolt-on patches into full-lifecycle components deeply embedded in large-model training and fine-tuning cycles. The firm believes that the share of security budgets in China’s AI investment structure will rise significantly, and the deep integration of the Chinese-made computing power supply chain with the Chinese-made security supply chain will jointly build the core barrier for China’s digital economy.
Driven by both policy and demand, Chinese cybersecurity companies are accelerating their deployment in the AI security space. According to Cailian Press’s theme database, NSFOCUS stated that the company has continued to strengthen its investment in AI security in recent years. In the first half of the year, driven by policy, business opportunities related to data security and AI security in the financial industry increased notably, with multiple projects already implemented or in progress.
DBAPPSecurity released Hengnao 3.0, China’s first security AI agent, which deeply integrates with multiple scenarios including data governance, asset identification, vulnerability detection, log tracing, and API security, building an intelligent protection system covering both cybersecurity and data security.
From an industry impact perspective, the implementation of the US mandatory reporting requirement may have a demonstration effect on the global AI security governance landscape. On one hand, AI companies previously had considerable autonomy in security incident disclosure; under the new rules, the timeliness and transparency of incident reporting will be brought under regulatory scrutiny, with compliance costs rising accordingly. On the other hand, this opens new market space for security vendors — as AI security shifts from passive response to proactive management, enterprise security investment in model training, inference deployment, and data flow will become more regularized.
For the Chinese domestic market, the expectation of a higher AI security budget share is even more explicit. As Chinese-made large models accelerate deployment in critical sectors such as government affairs, finance, and energy, whether security capabilities can be embedded throughout the model lifecycle has become one of the core metrics for procurement-side supplier evaluation. IDC’s 106.5% compound growth forecast reflects market consensus on the high-growth trajectory of the AI security sector.
It is worth noting that AI security differs significantly from traditional cybersecurity in technical approach. Traditional security focuses on perimeter defense and known-threat detection, while AI security must cover new risks such as model poisoning, prompt injection, data leakage, and inference-side attacks. This means security vendors need to extend their capabilities to the model layer and data layer, rather than deploying protection only at the network layer.
China Galaxy Securities further noted that the deep integration of the Chinese-made computing power supply chain with the Chinese-made security supply chain will be the key direction in the next phase. Against the backdrop of accelerating localization of computing infrastructure, if security capabilities cannot coordinate with Chinese-made chips, frameworks, and model stacks, they will struggle to meet the IT application innovation requirements of critical industries. Therefore, AI security is not merely a technical issue but is closely tied to macro topics such as supply chain security and data sovereignty.
From corporate actions, the AI deployments of leading security vendors have moved from proof-of-concept to productization. NSFOCUS’s project implementations in the financial industry and DBAPPSecurity’s multi-scenario integration of Hengnao 3.0 both reflect that security AI agents are evolving from single-purpose threat detection tools into comprehensive protection platforms covering data governance, asset identification, vulnerability detection, log tracing, and API security.
In the coming period, competition in the AI security sector will likely focus on three areas: first, the depth of scenario coverage by security agents, and whether they can truly embed into model training and fine-tuning processes; second, compatibility with the Chinese-made computing ecosystem, and whether they can pass IT application innovation certification and enter critical industry procurement catalogs; third, under compliance-driven demand, whether security capabilities can be converted into quantifiable return on investment, driving customer budgets to migrate from traditional security to AI security.
The introduction of the US mandatory reporting regime, combined with expectations of a rising AI security budget share in China, is injecting new growth momentum into the cybersecurity industry. For investors, vendors with full-stack AI security capabilities and proven implementation cases in critical industries are likely to be the first beneficiaries of this wave of demand release.
