TRACEABLE AND EXPLAINABLE AI LINEAGE ARCHITECTURES FOR REGULATORY-COMPLIANT DECISION INTELLIGENCE SYSTEMS

Abstract

The rapid deployment of artificial intelligence in high-stakes domains such as financial compliance, cybersecurity, and regulatory decision intelligence has intensified the need for transparent, auditable, and accountable AI systems. Traditional machine learning pipelines often lack sufficient traceability and governance mechanisms, creating significant challenges for regulatory compliance and post-hoc auditing. This study proposes a traceable and explainable AI lineage architecture designed to support regulatory-compliant decision intelligence systems. Using a Design Science Research methodology, the study develops and evaluates a prototype compliance framework that integrates ontology-driven data lineage tracking, cryptographic verification mechanisms, and policy-governed MLOps pipelines. The architecture incorporates blockchain-based audit trails, provenance-aware logging, and tiered human-in-the-loop governance mechanisms to ensure continuous observability and verifiable accountability across the AI lifecycle. Experimental evaluation demonstrates substantial improvements in traceability coverage, audit reconstruction accuracy, and compliance verification reliability across multiple system deployment cycles. The findings indicate that integrating provenance tracking with cryptographic integrity protocols enables organizations to generate verifiable regulatory evidence without exposing proprietary model parameters. By operationalizing compliance-by-design principles within AI infrastructure, the proposed framework bridges the gap between regulatory mandates and technical implementation. The study contributes to the growing field of trustworthy AI governance by providing a scalable architecture capable of supporting transparent, auditable, and legally compliant AI decision systems in complex regulatory environments.