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Together, these controls reduce stale or inappropriate inputs, limit unnecessary exposure of sensitive information, and make failures diagnosable. They are operating practices rather than a certification checklist; each organization still has to define controls that fit its systems, legal obligations, risk tolerance, and AI use cases.
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What AI data governance covers
AI data governance is the set of decision rights, controls, and operating routines that determine what data an AI system may use, why it may use it, how it is transformed, who is accountable for it, and when it must be removed. The scope follows data through its lifecycle: collection, storage, preparation, retrieval, model input, output evaluation, sharing, archiving, and deletion.
Traditional data controls often emphasize ingestion and storage. Bacancy’s approach adds a critical checkpoint at the moment an AI workflow consumes data. A record can have been accurate when stored yet become stale, irrelevant to a particular task, or unsuitable under a changed permission or business context. Governance therefore has to be continuous and purpose-specific.
Privacy, security, quality, and AI risk are interdependent. The OECD’s discussion of AI and privacy principles links data minimization, purpose limitation, retention, accuracy, transparency, security, and accountability. A governance program that handles only one of these dimensions leaves predictable gaps.
Bacancy’s ten practices at a glance
| # | Practice | Control objective | Useful evidence |
|---|---|---|---|
| 1 | Govern data at the point of AI consumption | Use data that is accurate, current, relevant, and permitted for the specific task. | Validation results, freshness checks, purpose and permission decisions. |
| 2 | Create separate data areas for AI projects | Keep project data scoped so unrelated or sensitive information is not available by default. | Project workspaces, access policies, dataset inventories. |
| 3 | Assess sources before connecting them | Establish provenance, permitted use, quality, and compliance status before onboarding. | Source assessments, approvals, contracts, quality reports. |
| 4 | Set rules for RAG knowledge sources | Control which documents are indexed, who can change them, and when they are reviewed. | Allow lists, document owners, version history, review schedules. |
| 5 | Track movement through AI workflows | Make sources and transformations traceable so teams can investigate outcomes. | Lineage graphs, transformation logs, run identifiers. |
| 6 | Assign clear data owners | Give named people authority and responsibility for quality, updates, approvals, and issues. | Ownership register, escalation path, service targets. |
| 7 | Create data agreements between teams | Align producers and consumers on collection, use, definitions, and change management. | Data contracts, interface specifications, change notices. |
| 8 | Maintain data documentation | Record what data means, where it came from, and how it must be handled. | Catalog entries, glossary terms, schemas, handling instructions. |
| 9 | Define retention practices | Delete or archive data when the approved need ends instead of keeping it indefinitely. | Retention schedules, deletion logs, legal holds. |
| 10 | Prepare for multimodal data | Apply appropriate controls to images, audio, video, documents, and sensor inputs as well as text. | modality-specific policies, consent records, processing logs. |
How to implement the ten practices
1. Validate data at the point of use
Do not treat an ingestion check as a permanent guarantee. Add a pre-use gate to each material AI workflow. Confirm that the data still meets the task’s definition of accuracy, completeness, freshness, relevance, and permitted use.
- Define acceptance tests for the use case: required fields, tolerated age, duplicate limits, and prohibited values.
- Check freshness and source status immediately before retrieval, prompt assembly, fine-tuning, or batch scoring.
- Record the decision and the reason when data is rejected, quarantined, or allowed with a warning.
- Revalidate after significant transformations, not only at the original source.
A customer-support assistant may need current product and policy information, while a forecasting model may legitimately use historical snapshots. The rule is not “newest is always best”; it is that suitability must be judged against the stated purpose.
2. Isolate data for each AI project
Create a project-specific data area—such as a governed workspace, schema, bucket, or catalog domain—with explicit membership and default-deny access. Include only datasets required for the approved objective. Separate development, testing, and production data where their permissions or sensitivity differ.
Use role-based or attribute-based controls to restrict who can view, export, index, or train on each dataset. Mask or tokenize sensitive fields when the task does not require the original values. Review inherited permissions from shared drives and data platforms; an AI service can accidentally expand access if it receives a broad connector identity.
3. Assess every source before onboarding
Require a source assessment before a connector, file collection, API, or partner feed becomes available to an AI system. The assessment should identify the origin, collection method, owner, update frequency, intended use, contractual limits, personal or confidential information, geographic restrictions, and applicable quality requirements.
Rank #2
Test a representative sample for schema consistency, missingness, duplication, outliers, and known bias. Obtain approval from the data owner and the relevant privacy, security, or compliance function when the risk warrants it. Keep the assessment with the source record so a later user can understand why the source was accepted and under which conditions.
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RAG systems can produce confident answers from an indexed document that is obsolete or wrong. Establish an allow list of collections and document classes that may be indexed. Specify who can approve additions, who may edit or withdraw content, and what review interval applies to each class.
- Attach owner, effective date, expiration or review date, version, and sensitivity metadata to each document.
- Remove superseded versions from retrieval or mark them explicitly so they cannot silently outrank current guidance.
- Run retrieval and answer evaluations after major corpus changes.
- Provide a way to quarantine a document quickly when an error is discovered.
For high-impact answers, require citations or source references in the user interface and define escalation when no approved document supports an answer. RAG governance controls the knowledge base; it does not by itself guarantee that a model will reason correctly.
5. Trace data movement and transformations
Maintain lineage from the original source to the dataset, embedding, prompt context, model input, and evaluated output whenever practical. Capture transformation code or version, run time, configuration, and identifiers that let investigators reproduce the path.
Lineage is especially valuable when an output is challenged. Teams can determine whether the problem began with a source record, a filter, a join, a redaction step, a chunking strategy, an embedding update, or a prompt assembly error. Keep logs proportionate to sensitivity: protect or minimize payloads while retaining enough metadata to investigate.
6. Name accountable data owners
Assign an owner for every critical dataset and knowledge collection. Ownership is an operating responsibility, not merely a contact field. The owner should have authority to approve use, define quality expectations, authorize changes, resolve incidents, and coordinate deletion or archival.
Rank #3
Publish an escalation route with backup contacts and response targets. Distinguish the data owner from the platform operator, model owner, privacy officer, and business sponsor; one person may hold several roles in a small organization, but the responsibilities should remain explicit.
7. Establish agreements between teams
When one team supplies data to another, write a data agreement or contract covering the shared definition, schema, allowed purposes, quality thresholds, update cadence, access method, incident notification, and change process. Include what the consumer must not infer from the data.
Version the agreement and notify consumers before breaking changes. A lightweight contract can prevent disputes over whether a field is optional, whether historical values are restated, or whether a downstream AI use is permitted. For external suppliers, incorporate contractual rights, restrictions, and deletion obligations.
8. Keep documentation usable
Documentation should let a new engineer, analyst, auditor, or business owner understand a dataset without relying on tribal knowledge. Record business definitions, schema, provenance, transformations, sensitivity classification, permitted uses, quality indicators, owner, and retention rule.
Maintain a business glossary for terms that commonly cause model errors. Link glossary terms to physical fields and pipeline assets. Put handling instructions next to the data in the catalog or workspace, not only in a policy document that practitioners rarely consult. Review documentation when a source, transformation, owner, or approved purpose changes.
9. Enforce retention and deletion
Define how long each category of source data, derived dataset, prompt context, embedding, evaluation record, and operational log may be retained. The period should follow the documented business need and applicable obligations, with exceptions for legal holds or investigations.
Automate deletion or archival where possible and record what was removed, when, and by which process. Include copies in development environments, backups, caches, vector indexes, and exported files. If deletion from a model or embedding index is technically delayed, document the limitation and the compensating control rather than claiming immediate erasure.
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10. Extend controls to multimodal data
Images, audio, video, documents, and sensor streams introduce different metadata, privacy, quality, and storage concerns. A voice recording may contain incidental speakers; video can reveal location or biometric characteristics; sensor data may identify a person through patterns even without a name.
Define modality-specific collection notices, consent or authorization requirements, preprocessing, redaction, access, retention, and quality checks. Preserve timestamps, device or capture context, and transformations needed to interpret the data. Test how compression, transcription, OCR, sampling, and format conversion affect downstream accuracy.
A practical operating workflow
- Describe the use case. State the decision or task, users, expected output, impact of an error, jurisdictions, and data needed.
- Classify the data. Mark personal, confidential, regulated, proprietary, public, and operationally sensitive elements.
- Map sources and owners. Record provenance, permissions, update cadence, quality signals, and accountable contacts.
- Design the project boundary. Create the workspace, roles, service identities, network paths, and environment separation.
- Define point-of-use checks. Set freshness, completeness, relevance, and permission gates for each retrieval or scoring run.
- Approve knowledge and connectors. Use allow lists, source assessments, document review rules, and change notifications.
- Instrument lineage. Capture source and transformation identifiers without retaining unnecessary sensitive payloads.
- Operate and monitor. Track quality failures, access events, stale documents, incidents, and overdue reviews.
- Exercise deletion. Test removal from primary stores, indexes, caches, exports, and backups according to the retention schedule.
- Reassess on change. Repeat the review when the model, prompt, source, purpose, geography, or data modality changes.
Choosing governance technology
Technology can make controls discoverable and repeatable, but no product substitutes for ownership, agreements, or decisions about permitted use. Bacancy identifies catalog and governance products such as Collibra, Alation, Informatica CDGC, Microsoft Purview, Atlan, Databricks Unity Catalog, DataHub, and Apache Atlas, along with quality tools including Great Expectations and Soda. These names are examples of categories, not an independent ranking or performance verdict; the cited material provides no comparative testing, pricing, or winner.
| Selection axis | Questions to ask |
|---|---|
| Catalog and glossary | Can users find approved datasets, definitions, owners, classifications, and permitted uses? |
| Lineage | Does it show movement through pipelines, transformations, indexes, and AI environments at the needed granularity? |
| Quality monitoring | Can teams define freshness, completeness, validity, and anomaly checks and route failures to owners? |
| Privacy and access | Does it integrate with identity, masking, policy enforcement, audit, and deletion workflows? |
| Platform integration | Will it connect to existing warehouses, lakehouses, APIs, vector stores, model platforms, and ticketing systems? |
| Operational fit | Can the organization staff stewardship, maintain metadata, and respond to alerts without creating an unmanageable process? |
Start with a high-value workflow and prove that the catalog, lineage, quality, and access signals are actionable. Expand only after owners can keep metadata current and incident routes work in practice.
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NIST describes the AI Risk Management Framework (AI RMF) 1.0 as voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation. The NIST framework page says it was released on January 26, 2023 and is being revised as part of the White House AI Action Plan. It is guidance, not a certification.
ISO/IEC 42001:2023 is an AI management system standard published in December 2023. ISO describes a Plan-Do-Check-Act system for addressing AI-related risks and opportunities across an organization. Bacancy’s FAQ contrasts NIST’s voluntary framework with ISO/IEC 42001’s certifiable management system and notes that certification is not required for every organization.
| Question | NIST AI RMF 1.0 | ISO/IEC 42001:2023 |
|---|---|---|
| Primary role | Risk-management guidance for trustworthy AI. | Requirements for an organizational AI management system. |
| Operating model | Flexible functions and outcomes that organizations tailor to context. | Plan-Do-Check-Act system with documented management processes. |
| Certification | Not a certification scheme. | Can support certification by an accredited conformity-assessment body. |
| Best use in this program | Structure risk identification, measurement, management, and governance discussions. | Formalize responsibilities, controls, audits, continual improvement, and evidence. |
Use either framework—or both—as an organizing layer around the ten data practices. Neither removes the need to make concrete decisions about sources, permissions, owners, lineage, RAG content, retention, and modalities.
Common failure modes and fixes
- “The warehouse is governed, so AI is covered.” Add point-of-use validation and check downstream copies, indexes, and prompts.
- One shared service account sees everything. Create project-scoped identities and least-privilege connectors.
- A RAG corpus grows without review. Use an allow list, owner, version, review date, and emergency quarantine path.
- No one owns a failed quality check. Assign a named owner, backup, severity, and response target before production.
- Lineage stops at the pipeline. Extend identifiers through chunking, embedding, retrieval, prompt assembly, and evaluation.
- Documentation becomes stale. Make metadata updates part of change management and review ownership regularly.
- Deletion removes the database row only. Include vector indexes, caches, exports, backups, logs, and development copies.
- Text controls are reused unchanged for media. Add modality-specific consent, redaction, metadata, quality, and retention rules.
Measuring whether governance is operating
Choose measures that show control performance rather than activity alone. Examples include the percentage of critical sources with an owner and current documentation; percentage of AI runs passing point-of-use checks; age of unreviewed RAG documents; time to quarantine an incorrect source; lineage coverage for high-impact workflows; overdue retention deletions; and mean time to resolve data incidents.
Set thresholds by risk tier. A low-impact internal assistant may tolerate a different review cadence from a system that influences employment, credit, health, safety, or legal outcomes. Review measures with business, security, privacy, and engineering stakeholders so improving a metric does not create a new exposure—for example, retaining more sensitive logs merely to increase lineage coverage.
Bottom line for practitioners
Bacancy Technology’s ten practices are most useful as a connected operating model: validate at use, limit by purpose, assess before connection, govern RAG knowledge, trace transformations, assign owners, formalize team agreements, document meaning, delete when the need ends, and design for every data modality. NIST AI RMF can provide voluntary risk-management structure, while ISO/IEC 42001 can provide a certifiable management-system path where an organization chooses it. The durable result is not a one-time checklist but a maintained chain of accountability from source to AI output.
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