  Large Language Models (LLMs) have been applied to automate cyber security
activities and processes including cyber investigation and digital forensics.
However, the use of such models for cyber investigation and digital forensics
should address accountability and security considerations. Accountability
ensures models have the means to provide explainable reasonings and outcomes.
This information can be extracted through explicit prompt requests. For
security considerations, it is crucial to address privacy and confidentiality
of the involved data during data processing as well. One approach to deal with
this consideration is to have the data processed locally using a local instance
of the model. Due to limitations of locally available resources, namely memory
and GPU capacities, a Smaller Large Language Model (SLM) will typically be
used. These SLMs have significantly fewer parameters compared to the LLMs.
However, such size reductions have notable performance reduction, especially
when tasked to provide reasoning explanations. In this paper, we aim to
mitigate performance reduction through the integration of cognitive strategies
that humans use for problem-solving. We term this as cognitive enhancement
through prompts. Our experiments showed significant improvement gains of the
SLMs' performances when such enhancements were applied. We believe that our
exploration study paves the way for further investigation into the use of
cognitive enhancement to optimize SLM for cyber security applications.
