Description

IT environments generate massive amounts of log data—from applications, servers, databases, and network devices—making it nearly impossible for teams to manually detect meaningful anomalies. Our Anomaly Detection Service for IT Logs leverages artificial intelligence to process and analyze these logs in real time, identifying unusual patterns, behaviors, and potential indicators of compromise (IoC) or system malfunction. The system applies unsupervised and semi-supervised learning models, such as autoencoders, isolation forests, and time-series models, to detect deviations from normal baselines. It continuously ingests log data through integrations with tools like Elastic Stack (ELK), Graylog, Fluentd, AWS CloudWatch, and Syslog sources. It then applies statistical and machine learning techniques to recognize spikes in errors, unexpected user behavior, failed authentications, latency changes, or rare API calls. Alerts are intelligently routed to the appropriate teams based on confidence scores and contextual analysis, reducing noise and false positives. The platform also includes visualization tools for event correlation, alert frequency, and anomaly root cause exploration. This proactive detection helps prevent issues before they escalate, enhancing security posture, operational awareness, and compliance across hybrid cloud and on-prem environments.