Gestão automatizada de infraestrutura de redes: desenvolvimento de um assistente digital baseado em IA para análise de dados

Abstract

In the technology sector, managing network operations and interpreting the vast volume of data generated by servers has become an increasingly critical challenge. To address this practically, tools that facilitate error detection and provide rapid responses are essential, as manual analysis is often hindered by information overload. This study aimed to develop a solution leveraging Arti cial Intelligence (AI) to assist system administrators through a digital assistant that enables natural language queries regarding network status. The project was deployed on a cloud server, integrating automation and databases to intelligently process event logs. For validation, a case study was conducted using a messaging application, evaluating the tool’s ability to identify issues and send automated alerts about the environment’s health. The results demonstrated that AI integration signi cantly reduced fault diagnosis time, allowing complex server log queries to be resolved in seconds. Furthermore, the automated alert system showed high precision in anomaly detection, preempting structural bottlenecks before they impacted the end-user experience. This study concludes that the proposed solution not only optimizes the operational workload of network administrators but also establishes a scalable and ef cient model for critical infrastructure management mediated by intelligent assistants.