AI-Driven Threat Detection and Response in Cybersecurity
Abstract
Artificial Intelligence (AI) has become a transformative force in cybersecurity, enabling proactive threat detection and efficient response mechanisms to combat the increasing complexity and volume of cyberattacks. This paper explores the role of AI-driven solutions in modern cybersecurity, focusing on their ability to analyze vast datasets, detect anomalies, and identify threats in real-time. Machine learning algorithms and deep learning models enhance traditional methods by providing adaptive defenses against evolving threats, including malware, phishing, and ransomware. AI also streamlines incident response by automating processes such as threat prioritization and root cause analysis, reducing response times and minimizing human error. Despite its potential, challenges such as algorithmic bias, false positives, and vulnerabilities to adversarial attacks remain critical concerns. This study synthesizes current advancements, practical applications, and emerging trends, highlighting how AI-driven threat detection and response systems are reshaping the cybersecurity landscape. By addressing challenges and optimizing implementation, AI can significantly enhance organizational resilience and secure digital ecosystems against sophisticated adversaries.