Modern cybersecurity platforms must process and display high-frequency telemetry such as network logs, endpoint events, alerts, and policy changes in real time. Traditional rendering techniques based on static pagination or fixed polling intervals fail under volume conditions exceeding hundreds of thousands of events per second, leading to UI freezes, dropped frames, or stale data. This paper presents an AI-assisted adaptive rendering framework that dynamically regulates visual update frequency, prioritizes semantically relevant events, and selectively aggregates lower-priority data using behavior-driven heuristics and lightweight on-device machine learning models. Experimental validation demonstrates a 45-60 percent reduction in rendering overhead while maintaining analyst perception of real-time responsiveness.

翻译：现代网络安全平台必须实时处理和显示高频遥测数据，例如网络日志、端点事件、警报以及策略变更。基于静态分页或固定轮询间隔的传统渲染技术在每秒超过数十万事件的高负载条件下会失效，导致用户界面冻结、丢帧或数据陈旧。本文提出一种AI辅助的自适应渲染框架，该框架能动态调节视觉更新频率，优先处理语义相关事件，并利用行为驱动启发式规则与轻量级设备端机器学习模型，对低优先级数据进行选择性聚合。实验验证表明，该框架在保持分析人员对实时响应性感知的同时，可将渲染开销降低45%至60%。