Revolutionizing Threat Detection and Response: The Role of Data-Driven AI in Cybersecurity

EasyChair Preprint 15170

Abstract

In the rapidly evolving landscape of cybersecurity, the integration of data-driven artificial intelligence (AI) has emerged as a transformative approach to enhancing threat detection and response mechanisms. This paper explores the deployment of AI algorithms that leverage extensive datasets to identify and predict potential cyber threats in real-time. By analyzing patterns and anomalies in network behavior, data-driven AI significantly improves the accuracy and speed of threat detection, thereby reducing response times to potential breaches.

We examine various machine learning models, including supervised and unsupervised learning techniques, assessing their effectiveness in classifying threats, automating responses, and adapting to emerging threats. Furthermore, we address challenges related to data privacy, algorithmic bias, and the necessity for continuous model training to keep pace with evolving cyber threats.

Through case studies and empirical evidence, this research underscores the critical role of data-driven AI in constructing resilient cybersecurity infrastructures capable of safeguarding sensitive information in an increasingly digital world. The findings highlight not only the potential of AI to revolutionize cybersecurity practices but also the imperative for organizations to adopt a proactive stance in their threat management strategies.

Keyphrases: Cybersecurity, Data-driven AI, Threat Detection

@booklet{EasyChair:15170,
  author    = {James William and Ayesha Noor and Hasnain Ali},
  title     = {Revolutionizing Threat Detection and Response: The Role of Data-Driven AI in Cybersecurity},
  howpublished = {EasyChair Preprint 15170},
  year      = {EasyChair, 2024}}