Published Paper Details:

Rapid digitization across industries has made cybersecurity a paramount concern for organizations worldwide. Modern computing environments generate enormous volumes of data including authentication logs, network flows, application events, and endpoint telemetry -- volumes that far exceed human capacity for manual inspection. Artificial Intelligence (AI) has emerged as a transformative force in cyber defense, enabling systems to learn behavioral patterns, identify anomalies, classify threats, and facilitate faster incident response. This paper presents a comprehensive examination of AI-driven approaches for real-time threat detection and prevention. Core topics include intrusion detection, malware analysis, phishing identification, user behavior analytics, and automated security operations. Classical approaches such as Random Forest and Support Vector Machine are examined alongside deep learning architectures including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformer models. The paper additionally addresses persistent challenges including data quality issues, adversarial robustness, model drift, explainability demands, and infrastructure constraints. Findings indicate that while AI substantially enhances detection capabilities, its effective deployment depends on robust data pipelines, continuous monitoring, and collaborative human-AI workflows.