Scalable Data Governance Models for AI-Powered Computing Architectures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.017 | 0.017 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it