  With the rise of complex cyber devices Cyber Forensics (CF) is facing many
new challenges. For example, there are dozens of systems running on
smartphones, each with more than millions of downloadable applications. Sifting
through this large amount of data and making sense requires new techniques,
such as from the field of Artificial Intelligence (AI). To apply these
techniques successfully in CF, we need to justify and explain the results to
the stakeholders of CF, such as forensic analysts and members of the court, for
them to make an informed decision. If we want to apply AI successfully in CF,
there is a need to develop trust in AI systems. Some other factors in accepting
the use of AI in CF are to make AI authentic, interpretable, understandable,
and interactive. This way, AI systems will be more acceptable to the public and
ensure alignment with legal standards. An explainable AI (XAI) system can play
this role in CF, and we call such a system XAI-CF. XAI-CF is indispensable and
is still in its infancy. In this paper, we explore and make a case for the
significance and advantages of XAI-CF. We strongly emphasize the need to build
a successful and practical XAI-CF system and discuss some of the main
requirements and prerequisites of such a system. We present a formal definition
of the terms CF and XAI-CF and a comprehensive literature review of previous
works that apply and utilize XAI to build and increase trust in CF. We discuss
some challenges facing XAI-CF. We also provide some concrete solutions to these
challenges. We identify key insights and future research directions for
building XAI applications for CF. This paper is an effort to explore and
familiarize the readers with the role of XAI applications in CF, and we believe
that our work provides a promising basis for future researchers interested in
XAI-CF.
