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

Akila Wickramasekara, Frank Breitinger, Mark Scanlon

TL;DR

The paper surveys the potential of large language models (LLMs) to enhance digital forensic investigations amid rising workloads and data complexity. It articulates a structured DF context, reviews foundational LLM/NLP technologies (including transformers, fine-tuning, multimodal capabilities, and action-oriented models), and maps LLM capabilities to each phase of the DF process. The analysis identifies opportunities to boost efficiency, traceability, and automation through DF-specific LLM applications, while rigorously examining risks such as hallucinations, bias, censorship, privacy, and cost, and emphasizing human-in-the-loop safeguards. It also discusses practical deployment avenues (DFaaS, retrieval-augmented generation, and autonomous agents) and highlights future research directions, including domain-specific LLMs, standardized evaluation, and ethical-legal frameworks. Overall, the work offers a roadmap for responsibly integrating LLMs into digital forensics to improve investigative throughput without compromising evidentiary integrity or legal admissibility.

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.

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

TL;DR

The paper surveys the potential of large language models (LLMs) to enhance digital forensic investigations amid rising workloads and data complexity. It articulates a structured DF context, reviews foundational LLM/NLP technologies (including transformers, fine-tuning, multimodal capabilities, and action-oriented models), and maps LLM capabilities to each phase of the DF process. The analysis identifies opportunities to boost efficiency, traceability, and automation through DF-specific LLM applications, while rigorously examining risks such as hallucinations, bias, censorship, privacy, and cost, and emphasizing human-in-the-loop safeguards. It also discusses practical deployment avenues (DFaaS, retrieval-augmented generation, and autonomous agents) and highlights future research directions, including domain-specific LLMs, standardized evaluation, and ethical-legal frameworks. Overall, the work offers a roadmap for responsibly integrating LLMs into digital forensics to improve investigative throughput without compromising evidentiary integrity or legal admissibility.

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.
Paper Structure (35 sections, 1 figure, 3 tables)

This paper contains 35 sections, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Digital Forensic Context
  3. Digital Forensic Process Models
  4. Existing Challenges in Digital Forensics
  5. Existing Work With LLMs in Digital Forensics
  6. Background of Large Language Models
  7. Natural Language Processing
  8. LLMs
  9. Architecture of LLMs
  10. Word to Vectors
  11. Transformer Models
  12. Specifically Trained LLMs
  13. Multi-modal Large Language Models (MLLMs)
  14. Large Action Models
  15. Capabilities of Large Language Models
  16. ...and 20 more sections

Figures (1)

  • Figure 1: Traditional digital forensic process model casey2011digital