Advancing Information Governance in AI-Driven Cloud Ecosystem: Strategies for Enhancing Data Security and Meeting Regulatory Compliance

This study explores adaptive information governance models to address critical challenges in AI-driven cloud environments, focusing on enhancing data security and achieving regulatory compliance. Existing frameworks often fail to account for the complexities introduced by AI and cloud integration, leaving significant gaps in incident response, privacy protection, and governance practices. To bridge these gaps, this research evaluates governance components—Privacy-Enhancing Technologies (PETs), ethical oversight, and incident response metrics—through advanced quantitative methods, including Structural Equation Modeling (SEM), Cox Proportional Hazards Modeling, and Difference-in-Differences (DiD) analysis. Key findings highlight the significant influence of incident response metrics (β = 0.51, p < 0.001) and PET integration (β = 0.25, p = 0.001) on governance effectiveness, with model fit indices (RMSEA = 0.04, CFI = 0.96) confirming the robustness of the proposed framework. Industry-specific vulnerabilities were identified, with retail and technology sectors experiencing a 25% increased risk of incidents due to minimal security controls. The adoption of PETs, such as federated learning and homomorphic encryption, significantly improved privacy compliance and data utility, particularly in high-risk sectors. The study recommends the integration of advanced security controls and PETs to mitigate risks and improve compliance, especially in vulnerable industries like retail and technology. It also emphasizes the continuous optimization of AI-driven incident response protocols to reduce the impact of emerging threats. Furthermore, ethical oversight should be prioritized to ensure fairness, accountability, and public trust in AI applications within cloud ecosystems. These actionable strategies provide a roadmap for organizations to achieve secure and ethically governed cloud environments.