B-E

Data - Governance

Summary

B to E: Retrieval coverage reports, ungoverned AI access paths, corpus risk profiles, abstraction tier compliance, seal-break events, and disclosure patterns provided to Enterprise AI Governance for the AI risk register and use case decisions.

E to B: Governance directives on which corpora may be used for which AI use cases, jurisdictions, and tenants, and governance decisions on acceptable abstraction tiers, redaction defaults, and disclosure constraints -- typically expressed via Pillar A policy or GRC workflows that then drive changes in Pillar B configuration.

Commons DraftEditorial research

Standards and Specifications

EU AI Act
NIST AI RMF
ISO 42001

This interface turns data operations design choices -- what corpora are reachable by AI for which users, and how AI abstractions control disclosure -- into explicit governance artifacts that E-AIG can understand, approve, and monitor. Pillar B must summarize which data sources are exposed via AI and where gaps exist such as shadow indexes, and must provide metrics on how often each clearance tier is applied, how frequently seal-breaks or overrides occur, and what sensitive content appears in AI outputs. In return, E-AIG defines which datasets are in or out of scope for specific AI use cases and what retrieval patterns are prohibited, and defines which abstraction tiers are acceptable, what redaction is required by regulation, and when human review is mandatory -- usually codified as Pillar A policy that Pillar B implements and enforces. When B-E is mature, both data access and disclosure behavior become explicit objects of governance with measurable compliance, rather than side effects of configuration and prompt engineering decisions.

Variants

Retrieval coverage and exposure reports

Pillar B periodically produces reports that list all corpora, tenants, and data domains configured for AI retrieval, including their classification levels and associated AI use cases, and delivers them to governance for review.

Requires consistent tagging and metadata across indexes, vector stores, and connectors so that reports present an accurate, comparable picture of exposure; governance tools must be able to ingest or reference these reports as structured data, not just documents.

Ungoverned path and shadow index detection

Retrieval teams identify and report access paths where AI can reach data that has not been formally approved by governance, such as experimental indexes, developer-owned stores, or misconfigured connectors.

Benefits from integration with discovery or DSPM tools to detect shadow data; requires a shared process whereby E-AIG can either bring such paths under governance or mandate their shutdown.

Use case-bound corpus whitelists and blacklists

E-AIG defines which corpora and data domains each AI use case may or may not draw from, and Pillar B implements these decisions as allow/deny lists in retrieval configuration and connection policies, typically via changes to Pillar A-governed entitlements or access rules.

Relies on stable corpus identifiers and use case taxonomies so that governance decisions can be applied consistently across environments; retrieval systems must support configuration that ties corpora to specific AI applications or agent profiles.

Geo, tenant, and regulatory boundary enforcement

Governance sets requirements for data residency, tenant isolation, and regulatory segmentation, and Pillar B encodes them as routing and filtering rules that prevent AI retrieval from crossing prohibited boundaries.

Depends on clear metadata about data location, tenant ownership, and regulatory tags, as well as retrieval engines that can enforce constraints at query time; misalignment between catalog tags and retrieval configuration is a frequent interoperability pitfall.

Governance attestation for retrieval configurations

Before new corpora or retrieval patterns go live, Pillar B submits configuration changes to E-AIG or a GRC workflow for approval, documenting how they satisfy governance policies and risk mitigations.

Requires integration between retrieval change processes and governance workflows, and the ability to reference specific configuration versions in governance records for later audit.

Abstraction compliance dashboards and reports

Pillar C aggregates metrics such as tier distribution, redaction frequency, and seal-break counts into reports and dashboards that governance reviews periodically as part of AI risk oversight.

Requires standardized metrics definitions and identifiers for abstraction tiers and use cases so that reports can be compared across applications and over time; governance tooling must be able to ingest or reference these metrics.

Seal-break and override governance review

Seal-break events and manual overrides logged by Pillar C are periodically reviewed by E-AIG to decide whether they indicate policy gaps, training issues, or acceptable exceptions.

Depends on including sufficient context in event records—such as use case, identity, and justification—while respecting privacy; governance decisions should feed back into both Pillar A policy and Pillar C implementation guidelines.

Governance-defined abstraction tier catalog

E-AIG maintains a catalog of approved abstraction tiers (for example, anonymized summary, pseudonymized detail, full content) and the conditions under which each tier may be used, which Pillar C implements as schemas and filters.

Requires a shared taxonomy for tiers and disclosure levels that both governance and engineering use; changes to the catalog must propagate to code, configurations, and testing to avoid drift.

Disclosure policy constraints for regulated data

Governance sets explicit rules for how regulated data types—such as health, financial, or children’s data—may be abstracted and revealed, and Pillar C encodes them in output classification and redaction workflows.

Needs clear mapping between legal categories and internal classification or label schemes; abstraction components must be able to recognize these categories based on input labels or detection results and apply the correct transformations.

Human-in-the-loop requirements for high-risk outputs

E-AIG defines which AI outputs require human review before release based on abstraction tier, data type, or audience, and Pillar C integrates review queues or approval steps into output delivery for those cases.

Requires workflow tooling that bridges AI applications and governance processes, plus metadata that signals which responses fall into human-review-required categories; careful UX design is needed to keep latency and reviewer burden manageable.