Strengthening Digital Security Against Social Engineering Attacks Using AI-Powered Behavioral and Predictive Detection Systems

Abstract

Social engineering attacks exploit human psychology to breach digital security, bypassing traditional technical defenses. The increasing sophistication of such attacks, coupled with the vast expansion of digital interaction, has highlighted the urgent need for innovative approaches to counter these threats. This paper explores the application of AI-powered behavioral and predictive detection systems to strengthen digital security against social engineering attacks. By leveraging machine learning, natural language processing, and behavioral analysis, these systems can detect subtle patterns indicative of malicious intent. Additionally, predictive analytics can anticipate potential threats based on historical data and user behavior. This research discusses the limitations of conventional security measures and evaluates the efficacy of AI-driven solutions through a detailed examination of their operational mechanisms. Key challenges, including data privacy concerns, adversarial attacks on AI models, and the ethical implications of user monitoring, are also addressed. The findings underscore that integrating AI-based detection with user education and robust policy frameworks provides a comprehensive defense against social engineering attacks. With the increasing reliance on digital communication and transactions, these systems represent a crucial advancement in safeguarding sensitive information and preserving user trust.