LLM-Powered Automated Cloud Forensics: From Log Analysis to Investigation

Cloud forensics is a crucial yet challenging field, as traditional forensic techniques struggle to handle the large-scale, dynamic nature of cloud environments. Manual forensic analysis is time-consuming, error-prone, and often fails to detect evolving cyber threats. This paper presents a novel tool leveraging Large Language Models (LLMs) to fully automate cloud forensic investigations. Our approach utilizes few-shot learning to classify log data, extract forensic intelligence, and reconstruct attack timelines. We evaluate LLM-based automation against traditional machine learning models, including Random Forest, XGBoost, and Gradient Boosting, using cloud forensic log datasets. Experimental results demonstrate that LLMs improve forensic accuracy, precision, and recall while reducing the need for extensive feature engineering. However, challenges such as hallucination risks, adversarial manipulation, and forensic explainability must be addressed to ensure the reliability of AI-driven investigations. To mitigate these risks, we explore Retrieval-Augmented Generation (RAG) for context-aware forensic intelligence and propose hybrid AI models integrating rule-based forensic validation. Our findings highlight the potential of LLM-driven forensic automation to enhance cloud security operations while outlining key areas for future research, including adversarial robustness, forensic transparency, and multi-cloud scalability.