Audit Chain: Block chain-Backed Green Audits Using Federated AI for Cross-Border Enterprises

Abstract

The accelerating integration of sustainability into global trade has intensified the demand for credible, transparent, and verifiable green audits—particularly for Indian enterprises operating across multiple regulatory jurisdictions. Traditional environmental auditing mechanisms remain fragmented, manual, and vulnerable to data manipulation, greenwashing, and jurisdictional inconsistencies. This paper proposes Audit Chain, an integrated framework combining blockchain-based audit immutability with federated artificial intelligence (AI) to enable secure, scalable, and privacy-preserving green audits for cross-border enterprises. Using simulated empirical data from Indian multinational firms across manufacturing, logistics, and export-oriented services, the study examines how federated learning enhances audit accuracy while preserving data sovereignty, and how blockchain ensures traceability, trust, and regulatory alignment. Quantitative analysis demonstrates significant improvements in audit reliability, compliance turnaround time, and cross-border transparency, while qualitative insights reveal strong managerial acceptance and governance benefits. The findings suggest that AuditChain represents a transformative pathway for institutionalizing trustworthy sustainability assurance in emerging economies, aligning corporate environmental accountability with global ESG expectations. The study contributes to green auditing literature by operationalizing decentralized intelligence and distributed ledger technologies within cross-border audit ecosystems.