Cloud-Native Data Governance in AI Personalization: A Framework for Integrating Consent Management and Access Control (Published)
The rapid adoption of artificial intelligence in personalization systems has created unprecedented challenges in data governance within cloud-native environments. As organizations increasingly rely on AI for delivering tailored user experiences, the need for robust frameworks that address privacy concerns and regulatory compliance has become critical. This article presents a comprehensive framework integrating consent management and data access control within cloud-native architectures. The framework addresses key challenges in maintaining performance at scale while enforcing comprehensive access controls, ensuring consistency across distributed consent management systems, and handling edge cases in AI processing pipelines. Through innovative approaches to encryption, attribute-based access control, and dynamic policy enforcement, organizations can achieve significant improvements in security posture, operational efficiency, and user privacy protection. The integration of these components enables enterprises to deliver personalized experiences while maintaining strict compliance with evolving data protection regulations across multiple jurisdictions, ultimately fostering trust and transparency in AI-driven systems.
Keywords: AI personalization security, Cloud-native data governance, consent management integration, distributed access control, privacy-preserving frameworks