<![CDATA[Personalized digital experiences have become a standard expectation for enterprise customers, yet personalization initiatives face a persistent tension between increasing contextual relevance and protecting customer trust. This paper develops a governance-aware personalization architecture that integrates Adobe Experience Manager with secure customer data management and artificial intelligence-driven recommendations. The work aims to establish an architectural approach in which personalization capabilities are constrained by privacy, transparency, accountability, and auditability requirements rather than implemented as independent controls. The approach synthesizes established principles of recommender systems, privacy-aware personalization, responsible artificial intelligence, structured content management, and customer-data governance into an integrated architecture. Key mechanisms include consent-scoped data segmentation, graduated personalization, structured content delivery, AI-based recommendation using permitted customer attributes, visible recommendation rationale, and governance-event logging. The resulting architecture establishes explicit relationships between data authorization, personalization intensity, recommendation inputs, content selection, and audit records. The authorized consent scope constrains the intensity of personalization, while recommendation processes are limited to permitted data inputs and provide interpretable rationales for the generated recommendations. Governance-relevant events are also recorded to support traceability, review, and accountability throughout the personalization lifecycle. The results of the design analysis indicate that privacy and personalization requirements can be incorporated into the same architectural flow rather than treated as competing or isolated functions. The architecture provides a structured basis for delivering contextually relevant digital experiences while maintaining governance constraints over customer data and automated recommendations. The findings support the use of governance-aware architecture as a practical design approach for enterprise personalization systems. However, empirical validation is still required to determine its effects on customer trust, conversion outcomes, recommendation accuracy, and the trade-offs between personalization intensity and privacy protection]]>
