Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency

TL;DR

This paper investigates how Large Language Models can enhance digital forensic investigations by addressing current challenges and improving efficiency, traceability, and overcoming technical and legal barriers.

Contribution

It provides a comprehensive review of LLMs in digital forensics, highlighting potential benefits and challenges of integrating these models into investigative processes.

Findings

LLMs can improve investigation efficiency and traceability.

Integration of LLMs faces challenges like bias, explainability, and legal issues.

Proper constraints are necessary for effective adoption.

Abstract

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.