Artificial Intelligence in Cloud Forensics: Automating Evidence Acquisition and Anomaly Detection in Distributed Environments

Abstract

Cloud computing has become a critical component of modern digital infrastructure, supporting services ranging from data storage and business applications to large-scale artificial intelligence systems. However, the distributed and ephemeral nature of cloud environments poses significant challenges for digital forensic investigators, particularly with respect to evidence acquisition, analysis, and legal admissibility. Conventional forensic methods, which often rely on manual log inspection and static data extraction, struggle to cope with the speed, volume, and volatility of cloud-based data.
This research explores the application of artificial intelligence to cloud forensics, focusing on how machine learning models and automated frameworks can enhance the acquisition of digital evidence and the detection of anomalies within distributed systems. By employing experimental simulations across Infrastructure-as-a-Service and Software-as-a-Service environments, the study evaluates the performance of AI-assisted workflows compared to traditional approaches. Key findings demonstrate that AI significantly improves evidence recovery, reduces analysis time, and enhances anomaly detection accuracy. A case simulation is presented to illustrate how AI can reconstruct malicious activity in cloud environments, correlating access logs, user behavior, and system metadata. The study concludes that integrating AI into cloud forensics not only strengthens technical capacity but also contributes to forensic readiness by providing investigators with scalable, adaptive, and legally defensible tools.