AI-Driven Cybersecurity Frameworks for Modern Enterprise Threat Detection and Response

Abstract

The digital transformation of modern enterprises has precipitated an unprecedented expansion of the attack surface, rendering traditional rule-based cybersecurity measures increasingly inadequate against sophisticated, polymorphic threats. This paper investigates the integration of Artificial Intelligence (AI) into cybersecurity frameworks, specifically focusing on enhancing real-time threat detection and automated incident response. We propose a conceptual layered framework where machine learning (ML) models for anomaly detection, natural language processing (NLP) for threat intelligence, and deep reinforcement learning (DRL) for autonomous response mechanisms operate in a unified orchestration layer. The analysis demonstrates that AI-driven frameworks significantly reduce Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) by enabling predictive analytics and behavioral profiling. Furthermore, this paper addresses critical challenges including model adversarial attacks, data privacy concerns in federated learning environments, and the imperative for explainable AI (XAI) in regulatory compliance. The findings suggest that while AI offers transformative potential, its successful implementation requires a hybrid architecture that combines human-centric oversight with algorithmic resilience, ultimately redefining enterprise security posture from reactive defense to proactive cyber-resilience.