Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
This is an incremental review focusing on law enforcement's ability to handle increasing digital forensic workloads.
The paper explores the potential of Large Language Models (LLMs) to improve digital forensic investigation efficiency by addressing challenges like bias and resource constraints, concluding that LLMs could enhance efficiency and traceability with appropriate constraints.
The ever-increasing workload of digital forensic labs raises concerns about law enforcement's ability to conduct both cyber-related and non-cyber-related investigations promptly. Consequently, this article explores the potential and usefulness of integrating Large Language Models (LLMs) into digital forensic investigations to address challenges such as bias, explainability, censorship, resource-intensive infrastructure, and ethical and legal considerations. A comprehensive literature review is carried out, encompassing existing digital forensic models, tools, LLMs, deep learning techniques, and the use of LLMs in investigations. The review identifies current challenges within existing digital forensic processes and explores both the obstacles and the possibilities of incorporating LLMs. In conclusion, the study states that the adoption of LLMs in digital forensics, with appropriate constraints, has the potential to improve investigation efficiency, improve traceability, and alleviate the technical and judicial barriers faced by law enforcement entities.