  The ability to detect log anomalies from system logs is a vital activity
needed to ensure cyber resiliency of systems. It is applied for fault
identification or facilitate cyber investigation and digital forensics.
However, as logs belonging to different systems and components differ
significantly, the challenge to perform such analysis is humanly challenging
from the volume, variety and velocity of logs. This is further complicated by
the lack or unavailability of anomalous log entries to develop trained machine
learning or artificial intelligence models for such purposes. In this research
work, we explore the use of a Retrieval Augmented Large Language Model that
leverages a vector database to detect anomalies from logs. We used a Question
and Answer configuration pipeline. To the best of our knowledge, our experiment
which we called RAGLog is a novel one and the experimental results show much
promise.
