Responsible Data Lake Governance Architecture for AI‑Driven Enterprises: Compliance, Lineage, and Trustworthy Data Pipelines

Abstract

Data Lakes are crucial for modern businesses to power their artificial intelligence (AI), machine learning, analytics, and data-driven decisions. As distributed data environments proliferated, however, many data governance issues arose, including data quality, data lineage, data privacy, data security, data bias, data regulatory compliance, and trustworthy AI development. This paper presents a Responsible Data Lake Governance Architecture for embedding governance as a fundamental component of enterprise data pipelines and AI workflows. The architecture features controlled data ingestion, quality assessment, metadata management, automated lineage tracking, privacy classification, access control, bias monitoring, policy enforcement, and continuous monitoring and observability of raw, curated and semantic data zones. AI-specific extensions include training-data governance, feature-store management, model-input validation, dataset versioning and risk assessment. Human oversight is also built into the framework with data stewardship, approvals, governance committees and audit. The proposed architecture integrates technical controls with organizational and compliance aspects, offering a scalable basis for trustworthy and responsible AI operations. By doing so, the approach allows organizations to increase the reliability of data, increase regulatory readiness, lower governance risks and increase the transparency and traceability of AI-driven decision making. In summary, good data lake governance is established as a crucial architectural feature for enterprise AI adoption and sustainability.