Cloud systems play a pivotal role in modern computing, demanding highly efficient and adaptable network topologies to meet the growing needs of scalability, resource utilization, and low latency. This paper explores the application of advanced graph coloring algorithms as a solution for optimizing network topology in cloud environments. Graph coloring techniques enable efficient task scheduling, load balancing, and energy conservation by mapping network resources to tasks while avoiding conflicts. The study delves into heuristic, AI-driven, and distributed graph coloring methods, highlighting their relevance to addressing the complexities of dynamic, large-scale cloud networks. This work demonstrates the superiority of graph coloring-based approaches over traditional optimization methods through case studies and comparative analyses. While challenges such as scalability, computational complexity, and real-time adaptability persist, future directions, including the integration of machine learning and quantum computing, offer promising avenues for enhancing network performance. This research underscores the transformative potential of graph coloring in shaping the next generation of cloud systems.

The security risks have changed quickly, hence new strategies have been called for in endeavoring to protect the data and the systems. Contained in this research, the view of artificial intelligence regarding corresponding changes in data engineering for improving cybersecurity is considered. Managing and analyzing large volume of cybersecurity data through conventional techniques become more and more difficult because of the new scales, complexity and evolving nature of new threats. The application of AI helps in the better handling of data engineering that include the process in data gathering, data cleaning, and real-time data analytics, enhancing threat, detection, response, and mitigations.

The choice of the research method is a mixed one; it combines case studies, experiments, and surveys to evaluate AI’s effects on major data engineering processes in cybersecurity. Neural networks and several other assembling techniques as regularized for the efficiency of processing big scale datasets in cybersecurity. Equally important, the issues and constraints of adopting AI solutions into these processes are also carefully discussed. As seen in the outcomes of this study, data engineering that utilizes AI falls short of only tradition techniques but is ahead of them in terms of accuracy, time and scalability. The findings also highlight the importance of the utilisation of AI in overcoming present and future cybersecurity threats; where AI can provide organisations with the necessary advantage and formidable security against cyber threats that they need.

The study also brought out issues of model biases, ethics and scalability which are some of direction that needs to be undertaken to realize actual integration. The potential of directions for future research is outlined with respect to the new trends in the development of AI tools and techniques in data engineering for cybersecurity, including xAI and FL. Overall, this research helps to close the gap between AI and cybersecurity data engineering and contribute to the construction of stronger and more preventive cybersecurity paradigms.