AI-Assisted Adaptive Rendering for High-Frequency Security Telemetry in Web Interfaces

TL;DR

This paper introduces an AI-assisted adaptive rendering framework for cybersecurity dashboards that dynamically manages visual updates, reducing rendering overhead by up to 60% while preserving real-time data perception.

Contribution

It presents a novel AI-driven approach combining heuristics and lightweight ML models to optimize high-frequency telemetry visualization in cybersecurity interfaces.

Findings

45-60% reduction in rendering overhead

Maintains real-time responsiveness for analysts

Effective prioritization of relevant security events

Abstract

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.