Governing the Privacy-Personalization Tension in AI-Driven Marketing Platforms: An Integrative Framework for Customer Data Platforms

Customer data platforms (CDPs) are increasingly positioned as the data and decision infrastructure of artificial intelligence (AI)-driven marketing. Yet the academic literature has not adequately explained when a CDP can reconcile privacy compliance with hyper-personalization, nor how this reconciliation depends on platform governance, consumer trust, data quality, and responsible AI controls. This article develops an integrative conceptual framework through a structured interdisciplinary literature review. The review covers marketing, information systems, platform studies, consumer behavior, data governance, privacy engineering, and AI ethics. A structured integrative review with a systematic search component and concept-centric synthesis produced a final analytical corpus of 70 sources, comprising 60 peer-reviewed publications, one book, eight authoritative legal, policy, or standards documents, and one industry source used only for descriptive context. The synthesis identifies CDP governance capabilities as the central antecedent of two complementary mechanisms: privacy assurance and customer-data quality. Privacy assurance strengthens consumer trust and willingness to disclose first- and zero-party data, while governed identity resolution, provenance, interoperability, and data minimization improve the reliability of inputs available to machine learning, recommendation systems, generative AI, and autonomous agents. The resulting personalization effectiveness influences customer and marketing outcomes, but the relationships are conditional on responsible AI controls, platform ecosystem governance, regulatory intensity, data sensitivity, consumer privacy orientation, and organizational maturity. Seven direct-effect propositions and a decomposed family of path-specific moderation and boundary propositions specify theoretically expected relationships without implying causal identification by the review itself. The article contributes by conceptualizing the CDP not as a neutral database or stand-alone compliance tool, but as a governed platform control plane that coordinates data rights, AI decisioning, and multi-actor accountability. The framework offers a research agenda and practical design principles for privacy-preserving, transparent, and accountable AI-driven marketing ecosystems.