A microservices-based endpoint monitoring platform with predictive NLP models for real-time security and hate-speech risk alerting

TL;DR

This paper introduces a scalable microservices platform that integrates endpoint telemetry and NLP models, like BERT, for real-time security and hate-speech risk alerting in organizations.

Contribution

It presents a unified, modular architecture combining telemetry collection with predictive NLP models for comprehensive, real-time security and compliance monitoring.

Findings

Transformer models like BERT achieve 87% accuracy in hate-speech detection.

The platform effectively detects data exfiltration and policy violations in real-time.

The architecture supports scalable, low-latency alerting using RabbitMQ and Redis.

Abstract

Organizations increasingly depend on endpoint devices and corporate communication channels, yet they still face critical risks such as sensitive data leakage, suspicious user behavior, and the circulation of hateful or harmful language in workplace contexts. Current solutions frequently address these issues in isolation (e.g., productivity tracking, data loss prevention, or hate-speech detection), limiting correlation across signals and delaying incident response. This work proposes a unified, microservices-based platform that collects endpoint telemetry and applies predictive natural language processing models to support real-time security and compliance alerting. The architecture is modular and scalable, relying on RabbitMQ for event ingestion and routing and Redis for low-latency data access and alert delivery. For text classification, transformer-based models such as BERT are evaluated for hate-speech risk detection, achieving an average accuracy of 87\%. Experimental results indicate that the proposed platform can promptly surface indicators of data exfiltration and policy violations while centralizing alert management, providing an integrated framework that combines monitoring, security analytics, and predictive capabilities.