Social engineering is one of the major cybersecurity risks that exploit human psychology to deceive individuals into revealing sensitive information. Unlike cyberattacks targeting software vulnerabilities, these schemes manipulate emotions, trust, and authority. Common tactics include phishing, pretexting, baiting, and reverse social engineering, where attackers trick victims into seeking help. In recent years, social engineering remains widespread, responsible for a large share of cyber breaches, with a high number of businesses reporting at least one attack. Advanced techniques like deepfake technology and Generative Artificial Intelligence (GenAI) make these scams more convincing, increasing the challenge of distinguishing real from fraudulent interactions. Organizations must implement comprehensive training to mitigate risks, emphasizing skepticism and verification. GenAI tools play a vital role in strengthening social engineering awareness by automating phishing detection, behavioral analysis, and deepfake identification. AI-driven chatbots enhance security training, while threat intelligence platforms monitor social media for fraudulent activity. By integrating these AI solutions, organizations can better detect, prevent, and educate employees about evolving cyber threats. An important part of effectively leveraging AI in this context involves accurately calculating and managing token usage, ensuring that models can process complex communications efficiently without compromising analysis depth or performance.