AI-Log Intelligence Platform
Ms. P. Sasikala
Assistant Professor
Dept. of Computer Science and Engineering
Sri Shakthi Institute of Engineering and Technology
Coimbatore, India
sasikalacse@siet.ac.in
Aswin C
Dept. of Computer Science and Engineering
Sri Shakthi Institute of Engineering and Technology
Coimbatore, India
aswinc22cse@srishakthi.ac.in
Hrithik M
Dept. of Computer Science and Engineering
Sri Shakthi Institute of Engineering and
Technology
Coimbatore, India
hrithikm23lcse@srishakthi.ac.in
Kamalesh M S
Dept. of Computer Science and Engineering
Sri Shakthi Institute of Engineering and
Technology
Coimbatore, India
kamaleshms23lcse@srishakthi.ac.in
Abstract - In modern software development and IT operations, real-time log monitoring and analysis play a critical role in maintaining system health, identifying failures, and ensuring rapid incident response. Traditional log management approaches rely on static dashboards or manual inspection, and the absence of structured severity classification means that low-priority INFO logs are treated with equal weight as critical ERROR events, effectively burying actionable alerts within noise. Most existing log viewers also lack API-driven backends, forcing developers to rely on local file access or SSH sessions that are slow and insecure in production environments. As systems grow more complex and distributed, the need for intelligent, automated log analysis platforms has become increasingly important. To address these challenges, this project proposes SentinelX, a full-stack log analysis and monitoring system that employs a RESTful Flask backend continuously serving structured JSON log data to a React frontend, enabling sub-second refresh cycles for near-real-time monitoring. The platform's severity-aware filtering engine automatically categorizes log entries into INFO, WARNING, and ERROR tiers, reducing mean time to detect critical incidents, while an integrated search module allows developers to perform keyword-based queries across thousands of log entries instantly without requiring complex query languages. The system further incorporates strict JSON schema validation at the API layer, eliminating runtime crashes caused by malformed log payloads before they reach the frontend. By consolidating log ingestion, filtering, search, and visualization into a single centralized platform, SentinelX significantly reduces context-switching overhead and promotes a proactive monitoring culture, allowing teams to identify performance degradation and error spikes before they escalate into outages.
Key Terms - SentinelX, Real-Time Log Monitoring, Log Analysis, Flask Backend, React Frontend, REST API, Severity Filtering, System Observability, Dashboard Visualization, Error Handling
