MitigAInt A Review and Framework for AI‐Based Threat Response in Cloud Infrastructure

Abstract— The growing dependability on the cloud
infrastructure in various sectors has also ushered in a greater
security challenge due to the widened storm that has been
consequently experienced to affect traditional security
models. At that, the approach of including Artificial
Intelligence (AI) into cybersecurity methods, hereinafter
referred to as "MitigAInt" in the context of this review, can
lead to potentially fruitful directions of threat prevention and
agile protection. The given paper will review AI-based threat
response systems in cloud in a comprehensive manner and will
review the available learning on the topic and propose the
modular framework (MitigAInt) that is expected to
strengthen resilience, scalability, and responsiveness.
MitigAInt focuses on hybrid AI and its mix of machine
learning, deep learning and reinforcement learning by
incorporating anomaly detection in real-time, predictive
analytics, and autonomous decision making. The suggested
approach provides the data ingestion pipelines, the layers of
hostile identification, and response automation modules.
Previous studies and analyses of current models support the
effectiveness of AI in minimizing the effect of a breach, the
time it takes to respond to the breach, and the occurrence of
false positives. Lastly, in this paper, we have looked at the
limitation, ethical considerations, and future prospects of

developing more intelligent, explainable, and trustworthy AI-
based cloud infrastructure security.