AI-Powered Cybersecurity: A Data-Driven Approach to Protecting Digital Assets

EasyChair Preprint 15167

Abstract

he escalating complexity and sophistication of cyber threats necessitate innovative and adaptive cybersecurity solutions. Artificial Intelligence (AI), coupled with data-driven techniques, offers a promising approach to enhance threat identification and response capabilities. By leveraging AI's ability to analyze vast datasets, identify patterns, and learn from past experiences, organizations can gain a significant advantage in detecting and mitigating cyberattacks.

This paper explores the various AI-powered data-driven techniques that can be applied to cybersecurity, including anomaly detection, machine learning, and natural language processing. These techniques enable organizations to:

• Detect anomalies: Identify unusual patterns in network traffic, user behavior, or system logs that may indicate a potential threat.
• Predict attacks: Utilize machine learning algorithms to forecast future cyberattack trends and patterns.
• Analyze threat intelligence: Extract valuable insights from threat intelligence data to gain a better understanding of emerging threats and vulnerabilities.
• Automate response: Implement automated response mechanisms to contain and mitigate threats in real-time.

By effectively utilizing AI-powered data-driven cybersecurity techniques, organizations can strengthen their defenses, reduce the impact of cyberattacks, and protect their valuable digital assets. This paper provides an overview of these techniques and their potential benefits, highlighting the importance of a comprehensive and proactive approach to cybersecurity.

Keyphrases: Cyberattacks, Cybersecurity, Threat Detection

@booklet{EasyChair:15167,
  author    = {Ayesha Noor},
  title     = {AI-Powered Cybersecurity: A Data-Driven Approach to Protecting Digital Assets},
  howpublished = {EasyChair Preprint 15167},
  year      = {EasyChair, 2024}}