Enhancing Cloud Security and Compliance through Artificial Intelligence: A Conceptual Framework

Abstract

As cloud computing becomes integral to modern digital infrastructure, ensuring its security and regulatory compliance has emerged as a critical concern for enterprises and governments alike. Traditional security mechanisms often fall short in addressing the dynamic and complex nature of cloud environments. This paper conceptualizes the transformative role of Artificial Intelligence (AI) in enhancing cloud security and regulatory compliance. We explore how AI techniques such as machine learning, natural language processing, and anomaly detection are being leveraged to proactively detect threats, automate policy enforcement, and ensure compliance with global data protection regulations like GDPR, HIPAA, and ISO/IEC standards. Through a conceptual lens, we propose a comprehensive framework that integrates AI-driven solutions within cloud ecosystems to improve threat intelligence, incident response, risk management, and regulatory auditing. The paper also analyses challenges such as algorithmic bias, data privacy concerns, and compliance with jurisdiction-specific standards that must be addressed to realize AI's full potential in this domain. The paper describes results of adopting AI in cloud environments which include real-time monitoring, operational cost savings, and improved adaptability to the growing data ecosystem.  However, the paper also highlights challenges, such as data governance, transparency of AI decisions, cross-border regulatory constraints, and ethical concerns surrounding algorithmic bias and explainability. The result of this study also describes how crucial human touchpoint is in the changing word of compliance. This study contributes to the growing body of literature by offering an integrative perspective on the symbiotic relationship between AI and cloud security/compliance, and by identifying future research directions that focus on ethical AI deployment, regulatory harmonization, and explainability in automated decision-making. The findings are particularly relevant to cloud service providers, IT policymakers, cybersecurity experts, and AI researchers