With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the pressing need for a structured and comprehensive overview of the current research landscape, enabling researchers to address emerging threats and vulnerabilities effectively. This paper presents a systematic mapping study (SMS), aimed at identifying and classifying the prevailing research topics, tools, and frameworks in the field of AI security. A total of 123 studies were meticulously selected and analyzed, leading to the identification of key metrics, tools, standards, and research themes that are currently shaping the landscape of AI security research. This effort not only aids in distilling the collective wisdom of the research community but also sets a firm foundation for future work in this critical area. The findings from this SMS will serve as an invaluable guide for researchers and practitioners alike, enabling them to navigate the complexities of AI security and fostering the development of innovative, robust security solutions. This study also highlights significant gaps in the current literature, thereby outlining potential directions for new research initiatives.