AI-Ready Data Governance in Microsoft Fabric with Purview

AI succeeds or fails on the trustworthiness of its data. In Microsoft Fabric, governance isn’t a layer added at the end—it’s the connective tissue that binds ingestion, transformation, analytics, and model consumption into a verifiable system of record. Purview provides the policy brain; Fabric provides the execution spine. Done well, governance here is not a brake on velocity—it’s how velocity scales without chaos.

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Why “AI-ready” governance is different

Traditional BI governance optimized for dashboards and quarterly reporting. AI raises the bar:

• Provenance: Models demand traceable inputs—who produced the data, how it was transformed, and under what assumptions.
• Contextual sensitivity: PII, PHI, contractual data, and licensing constraints must be machine-interpretable at training and inference time.
• Continuous change: Data and features evolve weekly; governance must adapt without re-writing policy for each dataset.
• Bidirectional risk: Not just “what went into a model,” but “what the model can reveal”—prompt leakage, memorization, and harmful inferences.

An AI-ready governance program embeds these concerns directly into the platform, not in documents or after-the-fact reviews.

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The Fabric–Purview contract

Think in terms of control planes versus data planes.

• Fabric is the data plane for OneLake (Lakehouses, Warehouses, Notebooks, Pipelines, Semantic Models, Real-Time analytics). It also exposes a governance-aware item model (workspaces, roles, endorsements, sensitivity labels).
• Purview is the control plane for metadata, lineage, classification, and policy across that estate (and beyond Fabric: SQL sources, SaaS mirrors, files). It makes policies portable and auditable.

The strategic outcome: policies follow the data wherever it moves—across ingestion (mirroring/shortcuts), transformation (notebooks/pipelines), modeling (semantic layer), and consumption (Power BI, applications, model training).

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Medallion layers as governance checkpoints

Medallion isn’t just a design pattern in Fabric; it’s a governance conveyor belt:

• Bronze (raw/landed):
  • Governance focus: classification at scale, coarse access boundaries, immutable lineage.
  • AI relevance: retention rules, legal hold, and consent tags travel with files from day one.
• Silver (standardized/clean):
  • Governance focus: quality SLAs (completeness, accuracy, timeliness), survivorship rules, reference data conformance.
  • AI relevance: feature candidacy begins here—inputs become predictable and benchmarkable.
• Gold (business-ready/curated):
  • Governance focus: certification, endorsement, fine-grained access, bias checks on curated attributes.
  • AI relevance: the trust boundary for training sets, retrieval corpora, and KPI-linked evaluators.

Treat each layer as a policy gate: nothing advances without passing its quality, lineage, and classification tests.

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Metadata as a product

Fabric’s items (Lakehouse tables, Warehouses, Semantic Models) are only as governable as their metadata. Purview turns metadata into a first-class product:

• Automated discovery & classification: tag PII/PHI/IP with confidence scores; apply sensitivity labels that propagate downstream.
• Business glossary & domain ownership: bake definitions and owners into the catalog so AI teams aren’t guessing what a “customer” is.
• End-to-end lineage: from mirrored source through notebooks/pipelines to Gold tables and Power BI models—critical for model audits and Right-to-be-Forgotten proofs.
• Data contracts: express expected schemas, distributions, and refresh cadences; contract violations raise governance incidents, not just pipeline failures.

The payoff is operationalized context: models and apps can enforce policy because the rules are machine-readable.

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Policy where it matters: at access and at movement

Two classes of control must coexist:

1. Access-time controls
   • Role-based & attribute-based access tied to Fabric workspaces and Purview labels (e.g., “Finance-Gold-Certified,” “Contains-PII:High”).
   • Row/column rules for least privilege (masking, filtering), so data scientists see only what they’re entitled to—without bespoke datasets for each persona.
   • Label propagation to consumption (reports, exports, model outputs), preventing “governed in storage, ungoverned in PowerPoint.”
2. Movement-time controls
   • Ingestion policy: who can create mirrors/shortcuts; allowable source regions; encryption expectations.
   • Transformation policy: approved compute, cost ceilings, and privacy-preserving transforms (tokenization, k-anonymity) enforced in pipelines or notebooks.
   • Egress policy: what can leave OneLake, under what label, and via which channels (APIs, exports, feature serving).

AI readiness requires both—who can see and how it flows.

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Data quality as a first-order SLO

Data quality often hides behind “we’ll fix it in feature engineering.” That erodes trust and inflates costs. In Fabric, quality should be observable:

• SLOs attached to tables (e.g., 99% completeness for core keys, 24-hour freshness for interactions).
• Lineage-aware impact: when a Silver metric drifts, Purview pinpoints which Gold features, dashboards, and models are affected; alerts route to owners.
• Golden path of tests: schema, distributional checks, deduplication rates, referential integrity—codified and versioned like code.

Models trained on fluctuating semantics are liabilities; quality SLOs make drift visible before it hits production.

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Sensitivity, privacy, and model safety—joined at the hip

AI governance extends beyond data storage:

• Sensitivity labels inform training pipelines: certain columns never join the feature store; others require irreversible hashing or differential privacy.
• Prompt/input governance for retrieval-augmented systems: the retriever enforces label-aware filtering so the model never sees restricted passages.
• Output governance & DLP: generated text inherits the highest sensitivity of its sources; downstream sharing respects that label.
• Data minimization by design: prefer aggregation, synthetic data, or federated patterns where possible; log justification for exceptions.

This connects privacy promises to model behavior—a requirement for audits and customer trust.

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The semantic layer is your contract with the business

In Fabric, Semantic Models (and their Power BI surfaces) are more than reporting artifacts; they’re authoritative definitions. Treat them as the last mile of governance:

• Certification & endorsement: only certified models feed AI-critical decisions or retrieval indexes.
• Metric governance: “revenue,” “active user,” “churn”—single definitions with lineage to source tables.
• Cost & consumption guardrails: throttle ad-hoc extract patterns; steer users to governed, cached artifacts.

When AI explanations reference a KPI, governance ensures it means the same thing across the enterprise.

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Real-time and unstructured: bring them into the tent

AI-ready governance must include fast and messy data:

• Streaming/real-time: apply labels and contracts at the event schema; enforce PII scrubbing at the edge; set retention windows aligned to regulation and model needs.
• Documents and vectorized corpora: content classification and usage rights flow into the vector index; retrieval respects both license and sensitivity.
• Feature stores / embeddings: treat them as governed assets with lineage to source columns and training snapshots.

If it feeds a model, it belongs under governance—regardless of format or speed.

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The operating model that sustains it

Technology won’t compensate for a weak operating model. Three patterns matter:

1. Federated domain ownership with a small central enablement team: domains own data products; the center owns policy, platform, and patterns.
2. Policy-as-code mindset: glossary, labels, access rules, and quality tests are versioned, reviewable, and automatable—no email-driven governance.
3. Metrics that matter:
   • Time-to-certify a data product
   • % of AI training data sourced from certified Gold
   • Data quality SLO attainment
   • Mean time to detect & remediate lineage-impact incidents
   • Cost per governed query / model training hour

These are business controls masquerading as data metrics.

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What “good” looks like

• Every AI dataset or feature set traces to a certified Gold source with documented lineage and quality SLOs.
• Sensitivity labels flow end-to-end—from ingestion to model outputs—and enforce themselves at access and movement.
• Purview catalog is the single pane of context: who owns it, what it means, where it came from, who can use it, and under which constraints.
• Fabric workspaces reflect domain boundaries, with clear roles, minimal standing privileges, and auditable endorsements.
• Governance is measured—not asserted—through SLOs, cost guardrails, and incident response tied to lineage.

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Bottom line: In Fabric, Purview turns governance from paperwork into platform behavior. That’s the prerequisite for trustworthy AI at scale: the right data, in the right hands, with the right meaning—proven, not presumed.