Enhancing Information Security Using Artificial Intelligence: A Next-Generation Defense Model

Abstract

The accelerating sophistication of cyber threats, coupled with the expanding attack surface of modern digital ecosystems, has rendered traditional information security (InfoSec) models increasingly inadequate. Signature-based detection, rule-based firewalls, and manual incident response mechanisms struggle to keep pace with polymorphic malware, zero-day exploits, and AI-powered adversarial attacks. This paper proposes a next-generation defense model that integrates Artificial Intelligence (AI) as a core, continuous, and adaptive layer within the InfoSec architecture. By leveraging machine learning (ML) for anomaly detection, deep learning (DL) for threat pattern recognition, and natural language processing (NLP) for contextual log analysis, the proposed model shifts from reactive defense to predictive and prescriptive security. Furthermore, the paper examines AI-driven automation in incident response, user and entity behavior analytics (UEBA), and adversarial AI countermeasures. It also addresses critical challenges, including data privacy, model explainability, and the risk of AI-powered attacks. The findings suggest that while AI profoundly enhances threat detection speed, accuracy, and resilience, a hybrid human-AI approach remains essential for strategic security governance.