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Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
Congratulations to Akila Wickramasekara and co-authors Frank Breitinger, and Mark Scanlon on the publication of Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency in Forensic Science International: Digital Investigation.
Co-authors: Frank Breitinger, and Mark Scanlon.
AI-generated summary of the contribution: This paper reviews recent advances in the application of Large Language Models (LLMs) within digital forensics, focusing on established models, methods, and key challenges. The study explores the potential of LLMs in improving digital forensic investigation efficiency, addressing challenges such as bias, explainability, censorship, and resource-intensive infrastructure. A comprehensive literature review highlights the current challenges in digital forensics and the possibilities of incorporating LLMs, with a focus on automation, investigative methods, and efficiency improvements facilitated by LLMs. The paper also discusses the limitations, ethical considerations, and forensic-specific risks associated with the use of LLMs in digital forensics.