International Journal on Science and Technology

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AI and ML for Automated Incident Resolution: Enhancing Speed and Accuracy

Author(s) Lakshmi Narasimha Rohith Samudrala
Country United States
Abstract Modern IT environments are extremely complex in nature. Traditional incident resolution methods which rely on manual troubleshooting, static alerts, and reactive monitoring are no longer sufficient to handle the complex IT Environment. Artificial Intelligence (AI) and Machine Learning (ML) provide abilities that can significantly improve the traditional incident management process.
This paper explores how AI-driven technologies leverage historical data to proactively identify issues, automatically detect root cause of the incident, and remediate them. This allows organizations to shift from reactive incident response to proactive and autonomous resolution strategies.
As organizations continue to grow, AI-powered incident management will play a crucial role in ensuring business continuity, operational efficiency, and a seamless digital experience for users.
Keywords Artificial Intelligence (AI), AI-Driven Incident Management, Machine Learning (ML), Automated Root Cause Analysis, Anomaly Detection, Self-Healing, Predictive Analytics, Intelligent Alert Correlation, Mean Time To Resolve (MTTR), Mean Time To Detect (MTTD), Event Correlation, IT Service Management (ITSM)
Field Engineering
Published In Volume 15, Issue 2, April-June 2024
Published On 2024-06-04
Cite This AI and ML for Automated Incident Resolution: Enhancing Speed and Accuracy - Lakshmi Narasimha Rohith Samudrala - IJSAT Volume 15, Issue 2, April-June 2024. DOI 10.71097/IJSAT.v15.i2.2839
DOI https://doi.org/10.71097/IJSAT.v15.i2.2839
Short DOI https://doi.org/g899gb
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