核心要点
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传统边界防御无法阻止机器级速度的威胁。
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快速的 AI 攻击能够绕过传统边界，使漏洞在混合环境中横向扩散。在 NIS2 框架下，未受遏制的威胁可能导致运营中断，而软件定义的微分段可以阻止横向移动。
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供应商权限过大，会导致监管层面的风险难以管控。
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传统 VPN 赋予第三方过大的内部网络访问权限，一旦凭据泄露，关键系统将面临严重威胁。NIS2 要求领导层承担责任，但基于身份的微分段可将供应商限定于特定工作负载，从而有效降低风险。
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对停机的担忧导致了危险的安全策略执行延误。
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安全团队通常对实施严格管控心存顾虑，因为一旦影响关键应用，就可能造成业务中断。长期暴露于风险之中会违反 NIS2 的业务连续性要求。借助仿真优先的测试方法，可以在规则上线前，基于真实流量数据进行数学级精确验证，从而有效化解这一矛盾。
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人工处理事件响应，难以在规定的报告窗口期内完成。
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传统工具往往需要数天才能完成入侵损失的溯源和评估，根本无法达到 NIS2 所规定的 24 小时内披露的要求。这会让董事会面临个人问责风险，但借助实时可视化映射和自然语言查询功能，可以即时评估出受影响的范围大小。
常见问题
常见问题
Traditional perimeters fail because AI-driven attacks compress exploitation timelines from days to minutes, allowing threats to move laterally faster than human security teams can discover and patch vulnerabilities.
It replaces broad network-edge VPN access with identity-driven microsegmentation that restricts vendor credentials to designated servers and ports, while embedding multi-factor segmentation directly into connection policies.
The primary obstacles are action paralysis driven by the fear of causing business disruption and organizational silos, as only 37% of enterprises report shared ownership of segmentation between security and infrastructure teams.
It runs draft security policies in a non-blocking mode against live traffic, allowing teams to mathematically validate that rules will not disrupt applications prior to activating blocking commands.
By using AI-powered labeling and pre-built templates, the platform enables organizations to achieve NIS2-ready segmentation status in weeks, compared to the years typically required by legacy segmentation projects.