AI-Driven Privacy, Security, And Trusted Commercialization Frameworks for Digital Commerce and Microservice Architectures

Abstract

The rapid integration of artificial intelligence (AI) into digital commerce, social e-commerce, small and medium-sized business (SMB) platforms, and microservice-based information systems has created a complex intersection of privacy, cybersecurity, commercialization, and operational governance requirements. Conventional approaches frequently address these dimensions independently, creating architectural and governance gaps when AI systems operate across distributed data environments and heterogeneous service infrastructures. This research develops a conceptual integrated framework that connects federated privacy-preserving analytics, zero-knowledge verification, trusted AI commercialization, incentive-aware recommendation infrastructure, and risk-based application security. The methodology synthesizes the provided literature to construct an architectural model consisting of privacy, intelligence, trust, security, and commercialization layers. Particular attention is given to the integration of federated learning with differential privacy and zero-knowledge verification in digital advertising, alongside multi-tenant AI infrastructure and hybrid SAST–DAST–SCA–IAST security assessment for microservice environments. The analysis indicates that privacy-preserving AI and security automation should be treated as mutually reinforcing components rather than isolated controls. The proposed framework also identifies trade-offs involving privacy utility, computational complexity, interoperability, tenant isolation, and governance. The study contributes a research-oriented architecture for organizations seeking to deploy commercially viable AI systems while maintaining privacy and security assurance across distributed digital ecosystems.