The Expanding Attack Surface: Securing AI and Machine Learning Systems in Security Operations

Abstract

Cyber threats' increasing magnitude and intricacy require a fundamental change in security operations. Conventional approaches face difficulties in keeping up, which exposes organizations to risks. This paper examines the expanding attack surface: securing AI and machine learning systems in security operations as a remedy. A literature review, informed by the Diffusion of Innovation Theory, investigates how organizations absorb innovations in this study. The results demonstrate notable benefits of AI/ML in security, such as superior identification of threats, improved efficiency through automation, and optimized management of vulnerabilities. Nevertheless, achieving successful execution necessitates meticulous deliberation of obstacles. These tasks encompass guaranteeing data accuracy, preserving the capacity to understand how models work, reducing any potential prejudices in AI/ML models, and resolving security weaknesses in the systems themselves. The paper also discusses ethical considerations and emphasizes the important function of human monitoring. To address these difficulties, the study recommends prioritizing data quality, utilizing explainable AI methods, and developing tactics to mitigate bias. Furthermore, there is a strong emphasis on using a human-in-the-loop strategy to take advantage of humans' expertise and machine-learning capabilities. This study highlights the capacity of artificial intelligence and machine learning to transform security operations completely. By confronting the recognized obstacles, organizations may unleash the genuine potential of these technologies and establish a stronger and more proactive security position in response to constantly changing cyber threats.