The proliferation of cyber threats has necessitated the evolution of network security defenses, with artificial intelligence (AI) emerging as a pivotal enhancement to these systems. This paper explores the integration of AI in cyber defense, focusing on various tools and techniques that leverage AI to bolster network security. Through a comprehensive literature review, the research underscores the utility of machine learning algorithms, pattern recognition, and anomaly detection in identifying and mitigating cyber threats more effectively than traditional methods. The methodology combines qualitative and quantitative analyses, including a detailed case study of AI's application in a real-world cyber defense scenario, which illustrates both the potential and the challenges of implementing AI technologies. This case study provides practical insights into the operational deployment of AI tools, their effectiveness in real-time threat detection, and the strategies for overcoming common pitfalls. Key findings highlight AI's capability to enhance the detection accuracy of security systems and its adaptability to new, sophisticated cyberattacks. Ultimately, the paper argues that AI is not just an auxiliary tool but a fundamental component of modern cyber defense strategies. It concludes with a discussion on future research directions, emphasizing the need for ongoing advancements in AI technologies to keep pace with the evolving landscape of cyber threats. This study serves as a crucial resource for cybersecurity professionals seeking to implement AI solutions and for policymakers formulating standards and regulations to govern AI use in cyber defense.

Artificial Intelligence, Cybersecurity, Network Security, Anomaly Detection, Machine Learning.