Authors: Nitin S. Kurup
Abstract: The accelerating sophistication of cyber threats has driven a paradigm shift from traditional signature-based defenses to intelligent, adaptive security frameworks powered by Artificial Intelligence (AI). Among these innovations, AI-enhanced endpoint protection has emerged as a pivotal mechanism for safeguarding organizational digital assets and ensuring resilience against evolving attacks. This review explores the multifaceted impact of AI-driven endpoint security systems on organizational resilience, emphasizing how machine learning, behavioral analytics, and automated remediation collectively enhance detection accuracy, response speed, and recovery capability. The study begins by examining the fundamentals of AI-based endpoint protection, detailing how technologies such as deep learning, natural language processing, and predictive modeling redefine threat detection and mitigation. It further analyzes how AI-driven security fosters organizational resilience through proactive threat anticipation, self-healing mechanisms, and real-time situational awareness. Comparative evaluations of leading AI-powered solutions—such as CrowdStrike Falcon, SentinelOne, Microsoft Defender, and Sophos Intercept X—illustrate substantial improvements in operational continuity and risk tolerance. Despite these advancements, challenges persist, including data bias, model transparency, adversarial AI, and ethical considerations surrounding automated decision-making. Addressing these issues is critical for sustainable and trustworthy adoption. Future research directions point toward federated learning, explainable AI, and quantum-resilient cybersecurity as pathways to more intelligent and ethical endpoint protection systems.
DOI: http://doi.org/10.5281/zenodo.17799978
