An Evaluation of Big Data-Driven Artificial Intelligence Algorithms for Automated Cybersecurity Risk Assessment and Mitigation

Abstract

The integration of big data and artificial intelligence (AI) has revolutionized the field of cybersecurity, offering innovative solutions to assess and mitigate risks in an automated manner. This paper evaluates the efficacy of big data-driven AI algorithms in the context of automated cybersecurity risk assessment and mitigation. It explores the intersection of big data analytics and AI, focusing on their ability to address challenges such as real-time threat detection, vulnerability analysis, and the deployment of countermeasures. The paper examines key algorithms, including machine learning (ML), deep learning (DL), and natural language processing (NLP), assessing their performance across dimensions such as accuracy, scalability, and adaptability. By analyzing the role of big data in enriching AI models with diverse and high-volume datasets, this paper highlights how these algorithms leverage advanced analytics to detect anomalies, predict cyber threats, and recommend remediation actions. Furthermore, it discusses challenges such as data privacy, algorithmic bias, and the computational complexity associated with processing large-scale data. The findings emphasize the transformative potential of big data-driven AI for reducing human dependency, improving detection rates, and enhancing resilience in cybersecurity frameworks. The paper concludes by identifying areas for improvement and future research, particularly in hybrid AI models and privacy-preserving computation techniques. This comprehensive evaluation offers insights into how these technologies are reshaping automated cybersecurity and their implications for organizational and global cybersecurity landscapes.