The article provides an extensive overview of cybersecurity challenges arising from the integration of Artificial Intelligence, particularly focusing on Adversarial Machine Learning (AML) vulnerabilities. It details various AML attack methodologies, such as data poisoning, evasion attacks, and prompt injection, which exploit inherent weaknesses in machine learning models, rendering traditional security tools insufficient. Furthermore, the text examines the distinct cybersecurity context in the United States, analyzing how regulatory frameworks, skill gaps, and the targeting of critical sectors influence the defensive landscape. Finally, the document reviews contemporary defensive strategies like adversarial training and certified robustness, while also discussing the practical implementation hurdles related to computational cost, performance trade‑offs, and the complex future threats posed by foundation models and physically realizable attacks.