NSFOCUS has received Frost & Sullivan’s 2026 Technology Innovation Leadership Recognition for its work in China’s AI security market, as enterprises increasingly move large language models, AI applications and autonomous agents from experimentation into production. The recognition focuses on the company’s Qingfengwei AI Security Product Matrix, which combines AI assessment, application protection, content safety, data-loss prevention and AI asset management.
The award places NSFOCUS among vendors attempting to solve a problem emerging alongside enterprise AI adoption: traditional cybersecurity controls were not designed around systems that can interpret prompts, generate content, invoke tools and make decisions autonomously.
Frost & Sullivan said its assessment considered two dimensions—strategy effectiveness and strategy execution—and credited NSFOCUS with aligning its technology development with changing requirements in China’s AI security market. The research and consulting firm specifically highlighted the company’s move from extending conventional security products toward what it describes as AI-native protection.
At the center of that strategy is the Qingfengwei AI Security Product Matrix. The portfolio includes AI-Scan for security and compliance assessment, AI-AFW for protecting AI application layers, AI-CONT for content safety, AI-DLP for preventing data leakage and AI-UTM for discovering AI assets and managing threats.
Rather than treating an AI deployment as another application to be protected, the architecture is designed around the different components of an AI environment. Those can include the underlying model, prompts, training and enterprise data, inference pipelines, applications, generated outputs and increasingly autonomous AI agents.
That distinction is becoming more important as companies experiment with agentic AI. An AI agent can do more than generate a response: depending on its permissions and integrations, it can retrieve information, call software tools, modify records or initiate workflows. A compromised or poorly governed agent therefore creates a security problem that extends beyond conventional application vulnerabilities.
NSFOCUS’s approach is organized around a three-phase protection framework called Radar Sweep, Real-Time Shield and Continuous Hardening. The first phase focuses on identifying AI assets and risks; the second protects interactions and data flows; and the third is intended to continuously improve an organization’s security posture.
The company also uses NSFGPT, a security-focused large language model designed for security reasoning, protocol parsing, threat analysis, work-order generation and remediation support. That places NSFOCUS in a broader enterprise AI security trend in which vendors are using specialized AI models to augment security operations rather than relying exclusively on general-purpose LLMs.
The timing reflects a larger shift in enterprise technology. McKinsey reported in late 2025 that 88% of surveyed organizations were using AI in at least one business function, while only 7% said AI had been fully scaled across their organizations. The gap suggests that companies are moving from isolated pilots toward broader deployments while still building the governance, infrastructure and security controls needed to operate AI at scale.
Cybersecurity spending is rising alongside that transition. Gartner forecast worldwide information-security spending of about $212 billion in 2025, up 15.1% from 2024, and predicted that 17% of cyberattacks or data leaks could involve generative AI by 2027.
For enterprises, the emerging AI security stack consequently spans several layers. Model and application security need to address prompt injection and other AI-specific attacks, while data-security controls must prevent sensitive information from reaching models or being exposed through generated responses. Organizations also need visibility into which AI systems and agents exist in their environments and what permissions they have.
NSFOCUS says its platform has been deployed in large and regulated environments, including more than 80 financial-sector deployments. The company claims its technology can handle up to 130 Gbps of traffic and 1.2 million events per second across a 60-node cluster, while reporting a reduction of up to 98% in alert noise in one banking deployment. Those figures are company-reported performance and deployment claims rather than independently verified benchmarks.
The company also reports nearly 130% growth in its AI security business between 2024 and 2026, a conversion rate above 60% from AI-Scan customers to full-platform deployments and customer retention above 95%.
Its support for appliance, containerized, on-premises and hybrid deployment models is particularly relevant in China, where regulated organizations may face data-sovereignty, infrastructure-control and compliance requirements that make a cloud-only security architecture impractical.
The competitive field, however, is broader than specialist AI security vendors. Cloud and enterprise technology providers including Microsoft, Google, Amazon and NVIDIA are building security capabilities into AI platforms and infrastructure, while cybersecurity companies are adding controls for LLM applications, data protection and AI agents.
That makes integration likely to become as important as individual detection capabilities. Enterprises adopting generative AI increasingly need security controls that work across models, applications, data stores, cloud infrastructure and autonomous agents rather than another isolated security product.
NSFOCUS’s recognition therefore reflects a larger industry transition: AI security is becoming a dedicated layer of enterprise AI infrastructure. As organizations move beyond experimentation, the ability to discover AI assets, monitor agent behavior, protect data and continuously assess model-driven applications will become increasingly important to making AI deployments operationally viable.
Market Landscape
The enterprise AI market is moving from experimentation toward broader deployment, increasing demand for AI security, data protection and governance. McKinsey found that AI use reached 88% of surveyed organizations in 2025, although only 7% reported fully scaling AI across the organization.
That creates an opening for AI security platforms covering LLM applications, AI agents, data leakage, model interactions and AI asset discovery. Gartner’s forecast of $212 billion in worldwide information-security spending for 2025 further illustrates the scale of the broader security market.
NSFOCUS is positioning Qingfengwei within this emerging category, competing indirectly with security capabilities being developed across cloud platforms, enterprise software and specialist cybersecurity products.
Top Insights
- NSFOCUS is positioning AI security as a dedicated enterprise layer covering models, applications, agents, data, content and AI assets.
- Its Qingfengwei portfolio combines assessment, application protection, content safety, data-loss prevention and unified AI asset management.
- The rise of autonomous AI agents expands the attack surface beyond traditional applications and increases the importance of runtime monitoring.
- Enterprise AI adoption is growing faster than organizational scaling, creating demand for security and governance infrastructure around production deployments.
- NSFOCUS’s reported deployment metrics show enterprise traction, although its performance figures remain company-reported rather than independent benchmarks.
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