Revolutionary Research On The AI Sentry: An Approach To Overcome Social Engineering Attacks Using Machine Intelligence

Abstract

This paper presents a review on revolutionary technology often referred to as ''The AI Sentry''. This is an advanced technological strategy to combat social engineering attacks through artificial intelligence (AI). Social engineering attacks present tremendous threats to individuals, organizations, and nations by having the ability to misuse psychological factors to manipulate the victims into providing confidential information or carrying out certain actions that compromise security [1]. The cybersecurity measures of recent time usually fail to discern as well as thwart such elaborate attacks because these attacks are deceptively carried out and due to the human flaws. The AI Sentry employs machine intelligence technologies such as behavioral pattern analysis, anomaly detection and social engineering attack deception to perform monitoring activities in real-time. Using Ai enabled functions in the cyber defenses [2], The AI Sentry emphasizes a proactive and adaptive methodology to increase security posture and immunity against social engineering attacks. Although the conventional methods of social engineering defense exhibited some success, they rely heavily on static rules and signatures, thus making it hard for them to keep pace with the fast evolving cyber criminals' tricks. Social engineering attacks have become more sophisticated and targeted making it necessary for the organizations to go beyond layered defense and equip themselves with more advanced and adaptive security measures such as machine learning based detection and behavior analytical tools to effectively deal with such issues. Nevertheless, the use of machine learning machinery in cybersecurity brings along challenges like reliability of data, model readability, and adversarial attacks. Ensuring that training data is provided with integrity and reliability is critical to avoid data biases and permit developing robust ML models. Besides, making sense of the inferences traced by highly nested neural networks proves to be a difficult task, resulting in debates in the realm of transparency and accountability.