The advent of Artificial Intelligence has unquestionably ushered in a brand new era brimming with possibilities across various domains. However, as with any transformative technology, it ushers in a wave of very complex challenges that demand our time and attention. In the intricate landscape of cybersecurity, AI has emerged as a very formidable ally, lending its abilities to the development of cutting-edge security strategies. One of AI's key strengths lies in its ability to analyze vast datasets and swiftly identify intricate statistical patterns that human analysts might overlook. This analytical prowess enables AI systems to detect anomalies, pinpoint vulnerabilities, and predict potential threats with incredible accuracy. Thus, it plays a pivotal role in boosting our digital defenses against the ever-evolving tactics employed by cybercriminals, “”We see AI as an integral part of cyber security strategy.”” (Srikanth 2023). Nonetheless, as AI becomes more deeply intertwined with decision-making processes in the cybersecurity domain, a potential vulnerability emerges. Malicious individuals, often ingenious scammers, could exploit this vulnerability by manipulating the data ingested by AI models. These subtle alterations might lead AI systems to make erroneous decisions, inadvertently aiding cyberattacks rather than thwarting them. This emerging back and forth between cybersecurity experts and malicious agents underscores the critical importance of securing the integrity of AI systems themselves. Beyond data manipulation, scammers have ingeniously harnessed the power of AI to craft malware that continually mutates and evolves. These AI-driven malware strains are adept at shape-shifting to evade conventional detection mechanisms. Consequently, because of the accessibility of AI, it poses a significant challenge for cybersecurity experts tasked with identifying and neutralizing these threats as it opens the door to more user generated malware in the open, “"Now, it mostly allows people who are not software developers to create malware. That makes the threat higher because at the end of the day there will be more malware criminals in the wild and more malware criminals will try and attack corporations."” (Dinzeo 2023). The dynamic nature of AI-driven threats necessitates continuous adaptation and innovation within the cybersecurity community. Countermeasures must not only keep pace with the evolving tactics of cybercriminals but also anticipate their next moves. To address this challenge, cybersecurity experts are increasingly turning to AI's own capabilities, utilizing machine learning and neural networks to develop more sophisticated and adaptive security systems.