Practitioner-driven Framework for AI Adoption in Digital Forensics

The impact of AI has not bypassed the field of digital forensics. However, despite the emergence of AI-based digital forensic methods and tools, their widespread adoption remains limited due to ethical, legal, and practical concerns. While existing research has proposed various solutions to support AI integration in digital forensics, many reiterate challenges already present in traditional digital forensics, focusing heavily on explainable AI, and often overlooking real-world feasibility. Thus, this study investigates the practical challenges affecting the adoption of AI in digital forensics by directly engaging with practitioners.To this end, we conducted a survey and interview study involving 28 digital forensic experts to explore their experiences with AI-based tools, their perceptions of AI in digital forensics, and the practical challenges they encounter. Our findings highlight key concerns related to validation, transparency, and the explanation and presentation of AI-generated evidence in court. We also find that practical challenges are often broader than those discussed in theory, warranting deeper, practice-oriented analysis and perspectives.Based on these findings, we propose a practitioner-focused framework to support stakeholders, including forensic professionals, developers, law enforcement, regulators, and researchers, in fostering standardized, responsible, and effective adoption of AI-based digital forensics. Rather than replacing existing procedures, our framework builds on traditional digital forensic processes, extending them to address AI-specific requirements. Finally, as part of this proposed framework, we provide practical recommendations for the development and deployment of AI-based digital forensic tools that are better aligned with real-world investigative needs.