  The escalating sophistication and volume of cyber threats in cloud
environments necessitate a paradigm shift in strategies. Recognising the need
for an automated and precise response to cyber threats, this research explores
the application of AI and ML and proposes an AI-powered cyber incident response
system for cloud environments. This system, encompassing Network Traffic
Classification, Web Intrusion Detection, and post-incident Malware Analysis
(built as a Flask application), achieves seamless integration across platforms
like Google Cloud and Microsoft Azure. The findings from this research
highlight the effectiveness of the Random Forest model, achieving an accuracy
of 90% for the Network Traffic Classifier and 96% for the Malware Analysis Dual
Model application. Our research highlights the strengths of AI-powered cyber
security. The Random Forest model excels at classifying cyber threats, offering
an efficient and robust solution. Deep learning models significantly improve
accuracy, and their resource demands can be managed using cloud-based TPUs and
GPUs. Cloud environments themselves provide a perfect platform for hosting
these AI/ML systems, while container technology ensures both efficiency and
scalability. These findings demonstrate the contribution of the AI-led system
in guaranteeing a robust and scalable cyber incident response solution in the
cloud.
