An Extensive Review of Artificial Intelligence Utilization in Data Science for Strengthened Cybersecurity Analytics, Predictive Threat Assessment, and Advanced Risk Management Strategies

Abstract

The rapid evolution of cyber threats in the digital era necessitates advanced, data-driven cybersecurity solutions. This review explores the transformative role of Artificial Intelligence (AI) in data science for enhancing cybersecurity analytics, predictive threat assessment, and advanced risk management strategies. By leveraging machine learning, deep learning, and natural language processing, AI enables real-time anomaly detection, accurate threat prediction, and automated risk prioritization, shifting cybersecurity from reactive to proactive paradigms. The integration of AI with threat intelligence platforms and robust data science practices, such as clustering and feature engineering, empowers organizations to process vast, heterogeneous datasets, detect sophisticated threats like advanced persistent threats (APTs) and ransomware, and mitigate risks efficiently. However, challenges including data quality issues, false positives, adversarial AI, and ethical concerns such as bias and privacy must be addressed. Emerging trends like explainable AI (XAI) and federated learning offer promising solutions for improving model transparency and data privacy. This paper underscores the strategic importance of AI in building resilient, adaptive cybersecurity frameworks and advocates for ongoing research to overcome limitations and ensure ethical AI adoption in safeguarding digital ecosyst