The Role of Artificial Intelligence in Cybersecurity: A Review of Threat Detection and Mitigation Techniques

Abstract

The rapid evolution of cyber threats has necessitated the development of advanced techniques for threat detection and mitigation. As cyberattacks grow in sophistication and frequency, traditional cybersecurity measures are increasingly inadequate. Artificial Intelligence (AI) has emerged as a pivotal tool in enhancing cybersecurity, offering innovative solutions to detect, analyze, and neutralize threats in real-time. This article provides a comprehensive review of the role of AI in cybersecurity, with a specific focus on its application in threat detection and mitigation. It explores various AI techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), and evaluates their effectiveness in identifying and countering cyber threats. ML algorithms, for instance, excel in pattern recognition and anomaly detection, while DL models are adept at handling complex, unstructured data. NLP, on the other hand, plays a crucial role in analyzing textual data to identify phishing attempts or malicious communications. Despite its potential, the integration of AI in cybersecurity is not without challenges. Issues such as data privacy, algorithmic bias, and the need for large datasets for training AI models pose significant limitations. Additionally, adversaries are increasingly leveraging AI to develop more advanced attacks, creating an ongoing arms race. The article also proposes future research directions, emphasizing the need for robust, explainable AI systems and collaborative frameworks to address emerging threats. To support the discussion, three related tables are included, summarizing key findings, comparing AI techniques, and presenting case studies of AI-driven cybersecurity solutions. This review underscores the transformative potential of AI in cybersecurity while highlighting the importance of addressing its limitations to ensure a secure digital future.