Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models

Abstract

Incident management for cloud services is a complex process involving several steps and has a huge impact on both service health and developer productivity. On-call engineers require significant amount of domain knowledge and manual effort for root causing and mitigation of production incidents. Recent advances in artificial intelligence has resulted in state-ofthe-art large language models like GPT-3.x (both GPT-3.0 and GPT-3.5), which have been used to solve a variety of problems ranging from question answering to text summarization. In this work, we do the first large-scale study to evaluate the effectiveness of these models for helping engineers root cause and mitigate production incidents. We do a rigorous study at Microsoft, on more than 40,000 incidents and compare several large language models in zero-shot, fine-tuned and multi-task setting using semantic and lexical metrics. Lastly, our human evaluation with actual incident owners show the efficacy and future potential of using artificial intelligence for resolving cloud incidents.

AI-Driven Cloud Security: Examining the Impact of User Behavior Analysis on Threat Detection

“…AI-driven security systems can analyze vast amounts of data from cloud environments to identify patterns, detect anomalies, and predict potential security incidents. These systems learn from ongoing activities, continuously improving their detection algorithms and adapting to new threats [24]. This transition is necessitated by the need for more proactive and predictive security measures in cloud environments.…”

“…Unlike traditional security measures, which often rely on predefined rules and signatures, AI-driven systems utilize machine learning algorithms to analyze patterns in data, enabling them to detect anomalies and potential threats that might go unnoticed by conventional methods [22,23]. For example, Ahmad et al [24] highlight AI's ability to detect zero-day attacks, which are new and unknown threats that traditional security measures often fail to recognize. AI systems can identify these threats by analyzing deviations from typical network behavior, making them highly effective in the ever-changing landscape of cybersecurity [12].…”