Social Engineering Attacks in Cybersecurity: Analysis, Challenges, and AI-Based Defense Framework

Abstract: As organizations migrate to complex cloud-native architectures, the human interface remains the most vulnerable point of entry. Traditional security mechanisms, while effective against automated malware, often fail to intercept sophisticated Social Engineering (SE) attacks that leverage psychological triggers. This research provides an in-depth analysis of modern SE vectors, including AI-generated phishing and deepfake-based impersonation. We propose a multi-layered AI-Based Defense Framework that utilizes Natural Language Processing (NLP) for semantic intent analysis and behavioral biometrics to create a "Human Firewall." The study evaluates the transition from static training to real-time, AI-driven intervention. Our findings suggest that integrating cognitive-aware AI systems can reduce the success rate of SE attacks by up to 85%, providing a robust defense against the evolving threat landscape of 2026.

Keywords: Social Engineering, Artificial Intelligence, Phishing, Deepfakes, Behavioral Biometrics, Human Element, Semantic Analysis.