A microservices-based endpoint monitoring platform with predictive NLP models for real-time security and hate-speech risk alerting
For organizations needing integrated endpoint security and hate-speech monitoring, this work provides a modular architecture that correlates multiple signals, though it is incremental.
The paper proposes a unified microservices platform that combines endpoint monitoring with NLP-based hate-speech detection, achieving 87% accuracy with BERT, to enable real-time security and compliance alerting.
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.