Alam, ShahidAltiparmak, Zeynep2026-02-272026-02-2720260952-19761873-676910.1016/j.engappai.2026.113892http://dx.doi.org/10.1016/j.engappai.2026.113892https://hdl.handle.net/20.500.14669/4657With the rise of complex cyber devices, Cyber Forensics (CF) is facing numerous new challenges. For instance, many systems are operating on smartphones, each hosting millions of downloadable applications. Analyzing this vast amount of data and making sense of it requires innovative techniques, particularly those from Artificial Intelligence (AI). To successfully implement these techniques in CF, it is essential to justify and explain the results to CF stakeholders. This will enable informed decision-making and foster trust in AI systems. An explainable AI (XAI) system can fulfill this role in CF, and we refer to this system as XAI-CF. Although XAI-CF is crucial, it is still in its early stages of development. Thus, there is a need to investigate and advocate for the importance and benefits of XAI-CF. Furthermore, previous studies do not provide a comprehensive survey of XAI's role in CF. Many only briefly address the challenges and ways to enhance XAI for this field, and none have proposed a unified framework for an XAI-CF system. To address these research gaps, this paper discusses the key requirements and prerequisites for an XAI-CF system. We present a thorough literature review of prior works that apply and utilize XAI to build and enhance trust in CF. In addition to discussing the challenges faced by XAI-CF and offering concrete solutions, we introduce the first XAI-CF framework that cohesively integrates XAI principles across every stage of the CF lifecycle. We believe that our work provides a solid foundation for future researchers interested in XAI-CF.eninfo:eu-repo/semantics/openAccessArtificial intelligenceCyber forensicsExplainable artificial intelligenceTrustworthy cyber forensicsExplainable artificial intelligence in cyberforensicsArticle167WOS:001680137600001