A surge of advancements in the AI sector has spiked cases of social engineering attempts around the world. The attacks have become increasingly harder to detect and more difficult to prevent. By using a systematic literature review, this thesis explores the differences between traditional and AI-enhanced methods by analyzing how AI tools can impact the efficacy of the attacks, such as realistic content creations, advanced targeting and automated attack infrastructures. The thesis also highlights the accessibility of AI based tools and how they can be used both as a safeguard but also pose an increased cyberthreat risk to an organization. 

Through comparative analysis, the thesis focuses on the key differences between conventional and AI-enhanced techniques, discussing the motivation behind why AI-enhanced are more prevalent today compared to the traditional ones. The paper also presents countermeasures that can be taken to mitigate these threats, such as adaptive policy changes, technical safeguards and increased awareness for the employees. 

Finally, the thesis aims to encourage the reader to better understand the modern social engineering tactics and methods, aiding the development of a more robust defense structure.