A Review of Applying AI for Cybersecurity: Opportunities, Risks, and Mitigation Strategies

Abstract

The issue with the evolving rapid development of complicated cyber threats has encouraged organizations to implement Artificial Intelligence (AI) and large language models (LLMs) as the revolutionary characteristics of contemporary cybersecurity development. These systems, through the use of machine learning, natural language processing and predictive analytics are able to perform automated code reviews, anomaly detection in real time, AI-based vulnerability assessments, intelligent analysis of threat intelligence. The potential of AI to handle huge amounts of information assists organizations in becoming even more proactive in detecting weaknesses, shortening the time spent responding to incidence, and even becoming more resilient. But at the same time, AI stands to pose a dual-use problem, encompassing such issues as adversarial attacks, insecure AI-generated code, and automated phish campaigns. This paper looks into mitigation measures including the human-in-the-loop systems, adversarial techniques, and governance frameworks like the NIST AI Risk Management Framework that maintain a balance between innovativeness and ethical governance. The paper concludes that the introduction of AI can vastly enhance cybersecurity even when carried out more judiciously and reinforced with robust governance that does not present an unmanageable.