Agentic AI as a Proactive Cybercrime Sentinel: Detecting and Deterring Social Engineering Attacks

Abstract

Social engineering attacks still remain the most persistent and adaptive forms of cyber threats in the modern digital world. Social engineering attacks are not the same as traditional cyberattacks, where it manipulates human behavior to gain access without permission, thus, security mechanisms that rely on technology become less effective. A new research in this paper proposes the use of "Agentic Artificial Intelligence (Agentic AI)" as a novel and proactive sentinel against social engineering attacks. The researchers believe that by utilizing the autonomy, goal-orientation, real-time learning, and situation-adaptability, the Agentic AI can act as a dynamic cybersecurity agent who is capable of detecting, analyzing, and preventing such threats. The study brings together natural language processing, behavioral analytics, and reinforcement learning as one agentic model to emphasize subtle linguistic and psychological characteristics that are similar to those of the implementers of social engineering. A simulation of a phishing and vishing scenario was used as a test bed for the evaluation of the performance of AI. The results show that Agentic AI goes beyond rule-based systems and traditional machine learning classifiers in the detection of social engineering attempts with a far greater accuracy and lower false alarm rate. This paper not only illustrates the methodology of incorporating agency into AI-powered cyber defense systems, but also declares that Agentic AI is not only capable of reacting but also that it can strategize and foresee thus, being capable of playing the role of a watchdog in the ongoing cyber war against human-centric attacks.