The Role of Machine Learning in Detecting and Preventing Data Breaches

Abstract

Data breaches pose a growing threat to individuals, organizations, and public safety. In response, machine learning (ML) has emerged as a powerful tool for both detecting and preventing these breaches. This research paper analyses the multifaceted role of ML in data security, exploring its strengths, limitations, and potential future directions. Through a comprehensive literature review, this paper examines how different ML models and algorithms can be employed to detect anomalous behavior, predict cyberattacks, and mitigate vulnerabilities. The importance of ethical considerations surrounding privacy and user rights is also addressed, emphasizing responsible data handling practices. Furthermore, the paper investigates the challenges and best practices of integrating ML into existing security infrastructure, providing recommendations for organizations seeking to leverage its benefits. Finally, it assesses the current limitations of ML in data breach detection and prevention, identifying promising research avenues for advancing data security through ML advancements. In conclusion, this research highlights the crucial role of ML in the ongoing battle against data breaches. By effectively utilizing its capabilities while maintaining ethical considerations, organizations can significantly enhance their data security posture and safeguard valuable information in our increasingly digital world.