DIGITAL FORENSICS & AI
Evaluating the Reliability of Digital Forensic Evidence Discovered by Large Language Model: A Case Study
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% 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.
Executive Impact & Key Findings
Our framework revolutionizes digital forensics by automating artifact extraction and refinement, ensuring high accuracy, traceability, and legal admissibility. It sets a new standard for AI-assisted investigations, providing robust, scalable, and auditable methodologies.
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Forensic Artifact F1-Score
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Artifact Extraction Accuracy
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Chain-of-Custody Adherence
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KG Connectivity Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Standardized Framework & Knowledge Graph Visualization
The proposed framework integrates database extraction, structured transformation, LLM refinement, and DFKG visualization to provide a holistic method for processing, analyzing, and validating forensic artifacts, ensuring evidence integrity for legal and investigative purposes.
Enterprise Process Flow
Identify Forensic Artifacts from Mobile Device Databases
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Transform Heterogeneous Databases into LLM-Readable CSV Format
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Construct Digital Forensic Knowledge Graph (DFKG)
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Apply Robust Forensic Metrics to Validate Evidence
Measuring Reliability: Accuracy & Coverage
The reliability of LLM-assisted forensic evidence is assessed using forensic-specific metrics, including Evidence Extraction Accuracy (EEA) at 95.24%, Forensic Artifact Precision (FAP) at 95.24%, Forensic Artifact Recall (FAR) at 100%, Evidence Consolidation Accuracy (ECA) at 92.31%, Knowledge Graph Connectivity Accuracy (KGCA) at 94.44%, Chain of Custody Adherence (CCA) at 100%, and Contextual Consistency Score (CCS) at 100%. These metrics confirm the framework's effectiveness in minimizing classification errors and ensuring comprehensive data retrieval.
97.56%
Overall Forensic Artifact F1-Score (FAF1)
Identifying Errors & Bias in LLM Evidence: Impact of Data Quality
The framework identifies key sources of error and bias including incomplete metadata, fragmented or deleted records, and contextual inference bias. Incomplete metadata, as seen with the timestamp 2021-06-25 02:29:19 EDT, was mistakenly linked to Twitter due to missing context. Fragmentation, like a corrupted email 19xxheisenbergcarro@gmail.comx1, can lead to misclassification despite accurate LLM refinement. Contextual bias leads to inferred relationships from co-occurrence rather than substantiated connections, e.g., CryptoWendyO@protonmail.com wrongly linked to Twitter, contributing to a KGCA of 94.44%. The framework mitigates these errors using confidence scores and manual graph-based hypothesis testing.
UID-Based Traceability & Validation
LLM-assisted forensic evidence is validated through deterministic Unique Identifiers (UIDs) and structured cross-referencing within the Digital Forensic Knowledge Graph (DFKG). Each artifact is assigned a UID derived from key attributes (database name, table name, file path, row index), preserving forensic provenance and ensuring end-to-end auditability. This maintains the chain of custody, enabling investigators to trace refined artifacts back to original raw sources. Empirical validation confirmed that every artifact retains its UID, leading to an Evidence Consolidation Accuracy (ECA) of 92.31% and 100% Chain-of-Custody Adherence (CCA). Inconsistencies, such as two incorrectly consolidated artifacts, were identified and corrected via UID-driven cross-referencing, demonstrating the system's ability to correct forensic errors.
| Practice | Benefit for Admissibility & Trust |
|---|---|
| Standardized Extraction (to CSV) | Ensures consistent formatting and enables LLMs to reconstruct fragmented artifacts while preserving essential metadata for chain-of-custody compliance. |
| UID-Based Traceability | Maintains provenance via SHA-256 hashing of device ID, database name, file path, table name, and row index, achieving 100% Chain-of-Custody Adherence. |
| LLM Refinement with Confidence Scores | Corrects inconsistencies, removes obfuscations, and filters low-certainty outputs (confidence score < 5) to minimize false positives, increasing Forensic Artifact Precision to 95.24%. |
| Robust Cross-Referencing (DFKG) | Affirms accurate relationship mapping with a Knowledge Graph Connectivity Accuracy of 94.44%, adhering to ISO/IEC 27037 guidelines for reproducibility and authenticity. |
Calculate Your Potential AI Impact
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Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A strategic phased approach to integrate and scale advanced AI capabilities for digital forensics within your enterprise.
Phase 1: Adaptive Thresholding & Deferred Review
Develop and implement adaptive confidence thresholding mechanisms and a deferred artifact review process to mitigate premature exclusions of valid evidence due to incomplete context or corruption.
Phase 2: Multi-Device Scaling & Real-World Cybercrime Assessment
Scale the framework to support multi-device investigations and conduct comprehensive performance assessments in complex, real-world cybercrime scenarios to ensure robust operational reliability.
Phase 3: AI-Driven Forensic Reasoning & Timeline Reconstruction
Integrate the framework with advanced AI-driven forensic reasoning systems to enhance classification accuracy, improve contextual understanding, and automate timeline reconstruction for complex cases.
Phase 4: STIX-Compatible Output for Interoperability
Implement export functionalities for UID-linked artifacts into Structured Threat Information Expression (STIX) formats, enabling seamless, standardized evidence exchange across diverse forensic tools and collaborative intelligence platforms.
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