AI-Driven Security Audits: Enhancing Continuous Compliance through Machine Learning

Abstract

The increasing complexity of digital infrastructures and the evolving landscape of regulatory requirements necessitate a shift towards more efficient and adaptive methods for security audits. Traditional manual auditing processes are time-consuming, error-prone, and often fail to keep pace with the rapid changes in cybersecurity threats and compliance standards. This paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate security audits, enhancing continuous compliance in dynamic environments. By integrating AI-driven tools, organizations can monitor security configurations, detect vulnerabilities, and identify policy violations in real time, reducing the risk of noncompliance. The research delves into key ML algorithms, including anomaly detection and predictive analytics, that allow for proactive identification of potential security issues and compliance gaps. Furthermore, the paper highlights the advantages of leveraging machine learning models that adapt to evolving regulatory standards, providing organizations with scalable, costeffective, and real-time audit capabilities. The findings demonstrate that AI-driven security audits not only streamline compliance processes but also improve the accuracy, efficiency, and responsiveness of cybersecurity measures, contributing to the overall resilience of organizations in an increasingly regulated digital world.