Securing Generative AI Data Pipelines in Multi-Cloud Environments: A Zero-Trust and Federated Learning Perspective

Abstract

The rapid adoption of generative artificial intelligence (GenAI) across industry verticals has concentrated value and risk within complex data pipelines that span multiple cloud providers. Securing these pipelines requires new paradigms that combine continual verification of identity and intent (Zero-Trust) with distributed, privacy-preserving model training and adaptation (Federated Learning). This paper presents a conceptual and technical synthesis for securing GenAI data pipelines in multi-cloud environments through a joint Zero-Trust and Federated Learning perspective. We (1) characterize threat vectors unique to GenAI pipelines — including data exfiltration during model ingestion, poisoning attacks on synthetic-data generators, and leakage from model outputs — in the context of multi-cloud service composition; (2) propose an architecture that integrates Zero-Trust controls (fine-grained identity and attestation, micro-segmentation, policy-driven least privilege) with federated training and secure aggregation protocols for model federation across heterogeneous clouds; (3) describe privacy, integrity, and provenance mechanisms (differential privacy, secure multi-party computation, hardware attestation, and blockchain-backed provenance) suitable for GenAI artifacts; and (4) outline evaluation metrics, attack scenarios, and an experimental plan to validate resilience, utility, and compliance. We conclude by identifying open research directions — notably adaptive trust scoring for federated participants, throughput-aware secure aggregation within heterogeneous cloud SLAs, and standards for generative model provenance — that must be resolved to operationalize secure, regulation-aware GenAI at scale. The synthesis emphasizes engineering trade-offs between privacy guarantees and generative utility, and argues that only a deliberate co-design of Zero-Trust enforcement and federated learning protocols can deliver secure, auditable GenAI pipelines across multi-cloud ecosystems