A Study on Using AI to Automate Compliance Auditing in Cloud Security Frameworks
Keywords:
cloud security, compliance auditing, machine learningAbstract
The rapid adoption of cloud computing has led to an increased reliance on cloud security frameworks to safeguard data and operations. As organizations increasingly leverage cloud environments, ensuring regulatory compliance and meeting security standards has become a significant challenge. Traditional manual auditing methods are time-consuming, error-prone, and often insufficient to keep pace with the dynamic nature of cloud infrastructures. Artificial Intelligence (AI) offers a promising solution to automate compliance auditing in cloud security frameworks, enhancing accuracy, efficiency, and scalability. This paper explores how AI technologies, including machine learning, natural language processing, and anomaly detection, can be applied to automate the auditing process in cloud security frameworks. We examine the benefits of AI-driven compliance auditing, such as real-time monitoring, improved threat detection, and reduced human error. Additionally, the paper discusses the challenges in implementing AI-based solutions, including data privacy concerns, algorithmic bias, and integration with existing security systems. Real-world examples of AI-driven compliance auditing tools are presented, highlighting their effectiveness in enhancing cloud security posture. The study concludes with an exploration of future directions for integrating AI into cloud security frameworks to improve automated compliance auditing.
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