Scalable Data Governance Models for AI-Powered Computing Architectures
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I3P101Keywords:
Data Governance, Policy-As-Code, Data Mesh, Data Contracts, Metadata Catalog, Lineage, Attribute-Based Access Control (ABAC), Federated Governance, Responsible AI, Model and Feature Governance, Multi-Cloud and EDG, Privacy and ComplianceAbstract
AI-powered computing architectures spanning cloud, edge, and on-device accelerators demand data governance models that scale across velocity, heterogeneity, and divergent regulatory regimes. This paper proposes a layered, policy-driven governance framework that separates a global control plane from distributed data planes to enable consistent enforcement with local autonomy. At the foundation, a metadata-centric “governance fabric” unifies catalogs, lineage, quality signals, and data contracts; on top, policy-as-code encodes access, purpose limitation, retention, and residency using declarative rules and continuous compliance checks. We synthesize patterns from data mesh and federated governance to support domain ownership without sacrificing enterprise guardrails, and introduce reference architecture with event-driven controllers, attribute-based access control, and consent/state propagation across services and models. For AI lifecycle coverage, the model extends to feature stores, embeddings, and artifacts, capturing provenance, drift, and evaluation results as first-class governance objects. Scalability is analyzed along organizational (domain autonomy, stewardship roles), technical (multi-cloud/edge deployment, schema evolution, streaming), and regulatory (cross-border transfer, sectoral rules) axes. We define operational metrics policy latency, lineage completeness, contract conformance, privacy risk, and auditability and present deployment guidance for phased adoption. The result is a pragmatic blueprint that enables high-velocity AI development while preserving trust, safety, and compliance through verifiable, automatable controls
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