{"id":36290,"date":"2026-06-03T06:59:58","date_gmt":"2026-06-03T06:59:58","guid":{"rendered":"https:\/\/nsfocusglobal.com\/?p=36290"},"modified":"2026-06-03T07:01:05","modified_gmt":"2026-06-03T07:01:05","slug":"nsfocus-interviewed-in-forresters-2026-navigate-the-ai-native-cloud-security-ecosystem-in-china-defining-the-digital-security-foundation-for-the-intelligent-era","status":"publish","type":"post","link":"https:\/\/nsfocusglobal.com\/pt-br\/nsfocus-interviewed-in-forresters-2026-navigate-the-ai-native-cloud-security-ecosystem-in-china-defining-the-digital-security-foundation-for-the-intelligent-era\/","title":{"rendered":"NSFOCUS Interviewed in Forrester\u2019s 2026 Navigate The AI-Native Cloud Security Ecosystem in China, Defining the Digital Security Foundation for the Intelligent Era"},"content":{"rendered":"
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SANTA CLARA, Calif., June 3, 2026 \u2013 On May 1, 2026, Forrester, the international research and advisory firm, released its latest trend research report Navigate The AI-Native Cloud Security Ecosystem In China<\/em>. The report systematically maps the development landscape of China\u2019s AI-native cloud security ecosystem and provides technology and security decision-makers with technical guidance for building highly resilient AI applications. NSFOCUS, as a representative of China\u2019s cybersecurity specialists, was successfully interviewed for the report. NSFOCUS believes it was included for its outstanding expertise and extensive real-world implementation experience in Cloud Security for AI and ModelOps full-lifecycle management, and was mentioned in the core model security section. To NSFOCUS, this not only validates its forward-looking technical vision in the AI security, but also demonstrates that its market strategy is highly aligned with the industry trends identified by leading international institutions.<\/p>\n\n\n\n

With the large-scale deployment of large language models (LLMs) and AI agents in the Chinese market, enterprise IT architectures are undergoing a historic transformation from \u201ccloud-native\u201d to \u201cAI-native cloud\u201d paradigms. In this transition, the foundational assumptions of traditional cloud security are being upended. Conventional approaches such as static workload hardening, network perimeter-based access control, and compliance-driven static configurations were all designed for predictable traditional applications. By contrast, in the AI-native cloud era of 2026, enterprises are faced with high-privilege AI agents capable of querying live data in real time, autonomously operating across hybrid and multi-cloud environments, and making decisions under uncertainty.<\/p>\n\n\n\n

The Symbiotic Relationship Between Cloud Security for AI and AI for Cloud Security<\/h2>\n\n\n\n

From a technological implementation perspective, \u201cAI-native cloud\u201d security is characterized by a dual-engine model driven by two complementary paradigms.<\/p>\n\n\n\n

Dimension<\/strong><\/strong><\/td>Core Definition & Protection Target<\/strong><\/strong><\/td>Defense Technologies<\/strong><\/strong><\/td>Typical Security Value & Outcomes<\/strong><\/strong><\/td><\/tr><\/thead>
Cloud Security for AI<\/td>Establishes a trusted cloud base for AI models and AI agents, protecting assets from theft, contamination, or hijacking.<\/td>Fine-grained access control, data lineage tracking, prompt filtering, model watermarking, and heterogeneous computing cluster hardening.<\/td>Prevents model poisoning and sensitive data leakage; ensures compliance of AI applications and controllable decision-making process under uncertainty.<\/td><\/tr>
AI for Cloud Security<\/td>Leverages AI to enhance the defense capabilities of cloud infrastructure itself and achieve intelligent resilience.<\/td>Machine learning-based telemetry analysis, workload behavior baseline modeling, and dynamic automated vulnerability remediation.<\/td>Eliminates configuration blind spots caused by cloud-native complexity; enables high-precision anomaly prediction and automated real-world response.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

For cybersecurity vendors, only by deeply understanding and mastering the symbiotic relationship between these two paradigms can they build a resilient digital sovereignty defenses against emerging threats while advancing the deployment of LLMs.<\/p>\n\n\n\n

NSFOCUS: Proven Metrics and Real-World Product Effectiveness<\/h2>\n\n\n\n

As a leading cybersecurity specialist, NSFOCUS boasts prominent expertise in Model Security for model runtime protection and ModelOps lifecycle governance..<\/p>\n\n\n\n

Quantified Operational Outcomes from Telecom Customers<\/strong><\/p>\n\n\n\n

A telecom operator faced a series of representative AI-native security challenges while integrating LLMs and AI agents into its core business. Due to limited visibility into LLM assets and insufficient control over behavioral boundaries of AI agents, the operator was frequently targeted by prompt injection and data leakage threats against LLMs. At the same time, unplanned token resource consumption within its computing infrastructure continued to drive up.<\/p>\n\n\n\n

By deploying NSFOCUS AI Unified Threat Management (AI-UTM), the operator built a full-lifecycle defense platform for LLM applications and achieved the following industry-leading results:<\/p>\n\n\n\n