Scalable Data Governance Models for AI-Powered Computing Architectures
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".