Evaluating the Reliability of Digital Forensic Evidence Discovered by Large Language Model: A Case Study
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| Format: | Preprint |
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2026
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| _version_ | 1866910030392983552 |
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| author | Khatiwala, Jeel Piyushkumar Addai, Daniel Kwaku Ntiamoah Xu, Weifeng |
| author_facet | Khatiwala, Jeel Piyushkumar Addai, Daniel Kwaku Ntiamoah Xu, Weifeng |
| contents | The growing reliance on AI-identified digital evidence raises significant concerns about its reliability, particularly as large language models (LLMs) are increasingly integrated into forensic investigations. This paper proposes a structured framework that automates forensic artifact extraction, refines data through LLM-driven analysis, and validates results using a Digital Forensic Knowledge Graph (DFKG). Evaluated on a 13 GB forensic image dataset containing 61 applications, 2,864 databases, and 5,870 tables, the framework ensures artifact traceability and evidentiary consistency through deterministic Unique Identifiers (UIDs) and forensic cross-referencing. We propose this methodology to address challenges in ensuring the credibility and forensic integrity of AI-identified evidence, reducing classification errors, and advancing scalable, auditable methodologies. A comprehensive case study on this dataset demonstrates the framework's effectiveness, achieving over 95 percent accuracy in artifact extraction, strong support of chain-of-custody adherence, and robust contextual consistency in forensic relationships. Key results validate the framework's ability to enhance reliability, reduce errors, and establish a legally sound paradigm for AI-assisted digital forensics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_20202 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating the Reliability of Digital Forensic Evidence Discovered by Large Language Model: A Case Study Khatiwala, Jeel Piyushkumar Addai, Daniel Kwaku Ntiamoah Xu, Weifeng Cryptography and Security Artificial Intelligence D.4.6; K.6.5; I.2.7 The growing reliance on AI-identified digital evidence raises significant concerns about its reliability, particularly as large language models (LLMs) are increasingly integrated into forensic investigations. This paper proposes a structured framework that automates forensic artifact extraction, refines data through LLM-driven analysis, and validates results using a Digital Forensic Knowledge Graph (DFKG). Evaluated on a 13 GB forensic image dataset containing 61 applications, 2,864 databases, and 5,870 tables, the framework ensures artifact traceability and evidentiary consistency through deterministic Unique Identifiers (UIDs) and forensic cross-referencing. We propose this methodology to address challenges in ensuring the credibility and forensic integrity of AI-identified evidence, reducing classification errors, and advancing scalable, auditable methodologies. A comprehensive case study on this dataset demonstrates the framework's effectiveness, achieving over 95 percent accuracy in artifact extraction, strong support of chain-of-custody adherence, and robust contextual consistency in forensic relationships. Key results validate the framework's ability to enhance reliability, reduce errors, and establish a legally sound paradigm for AI-assisted digital forensics. |
| title | Evaluating the Reliability of Digital Forensic Evidence Discovered by Large Language Model: A Case Study |
| topic | Cryptography and Security Artificial Intelligence D.4.6; K.6.5; I.2.7 |
| url | https://arxiv.org/abs/2602.20202 |