How to Align Data Governance With Enterprise Risk

How to Align Data Governance With Enterprise Risk

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Enterprise risk has become inseparable from data. Cyber incidents, AI model failures, misreported KPIs, and privacy breaches all originate in how data is created, transformed, and consumed. Yet in many enterprises, risk and data governance still run as separate programs.

The result is familiar. Risk registers talk in terms of ‘model risk’ or ‘financial misstatement,’ while governance teams inventory tables, dashboards, and pipelines. Audit findings sit in one system. Data issues sit in another. Neither side gets the full picture they need to act.

This guide lays out a pragmatic approach to align data governance with enterprise risk, using familiar risk concepts and practical governance building blocks. It is written for data leaders, governance leads, and risk/compliance teams who need to work together without starting from scratch.

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Why aligning data governance with enterprise risk matters now

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Most large organizations already have mature enterprise risk management and compliance programs. Many also have some form of enterprise data governance. The problem is that these efforts often run in parallel, using different language, tooling, and incentives.

Aligning them turns governance from a generic ‘good practice’ into a concrete response to specific enterprise risks and regulatory expectations. It also gives data leaders a clearer mandate and budget aligned to ERM priorities, as emphasized in the COSO Enterprise Risk Management framework.

Modern, adaptive approaches to governance make this alignment easier to operationalize across fast-changing data and AI estates (for example, Atlan’s adaptive data governance and data governance framework).

1. How risk and data realities are converging

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Enterprise risk frameworks increasingly recognize data, models, and technology as key risk drivers. Model risk, conduct risk, operational resilience, and third-party risk all depend heavily on data quality, lineage, and access, reflected in supervisory expectations like BCBS 239.

At the same time, cloud data platforms and AI systems have made data estates more complex and faster-changing. Traditional spreadsheet-based controls are no longer sufficient for real-time risk monitoring and regulatory expectations. Recent research on the state of enterprise data and AI shows that context, governance, and data quality are now central to scaling AI safely and effectively - Atlan State of Enterprise Data & AI 2025.

Modern governance programs therefore need to understand not only what data exists, but which data directly underpins critical reports, models, and decisions in the risk universe.

2. What risk leaders expect from data governance

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From a CRO or Chief Compliance Officer viewpoint, data governance is not an abstract maturity model. It is a set of capabilities that:

• Ensure key risk, finance, and regulatory metrics are accurate, timely, and reproducible, in line with expectations such as BCBS 239.
• Provide traceable lineage from board-level KPIs back to source systems and controls.
• Demonstrate to regulators that the organization understands where regulated data lives and how it is protected, for example through an accountability approach aligned with the UK ICO’s Accountability Framework.

Data leaders can translate these expectations into targeted initiatives using proven governance patterns like business glossaries, domain ownership, and cataloging, supported by Atlan’s data governance framework and data catalog.

3. Common misalignments between risk and data teams

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Several recurring gaps prevent effective alignment:

• Different languages: Risk talks in scenarios and impact; data teams talk in schemas and pipelines.
• Fragmented tooling: Risk issues in GRC tools; data issues in Jira or in a catalog; access in IAM; models in MLOps platforms.
• Unclear ownership: No single role accountable for a risk across both process and data dimensions.

Acknowledging these misalignments upfront makes it easier to design governance elements that naturally bridge ERM and data domains, rather than adding more silos, in line with the organizational principles in ISO 31000.

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Map enterprise risks to data, systems, and processes

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The first practical step is to treat data as a risk object, on par with processes, legal entities, and products in the ERM framework. That means starting from the enterprise risk register, not the data catalog, and systematically connecting each material risk to data, as recommended in COSO ERM.

This section outlines a simple, workshop-driven approach to build a ‘risk-to-data’ map that both risk and data teams can own and use.

1. Start from the enterprise risk register

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Begin with the existing enterprise risk register, including top risks, risk appetite statements, and key risk categories, consistent with ISO 31000.

For each high or medium risk, identify:

• The reports, models, processes, and decisions that evidence or manage that risk.
• Any regulatory mandates that explicitly mention data or reporting requirements (for example, BCBS 239).
• Known past incidents or audit findings tied to data quality, access, or lineage.

Capture these in a simple spreadsheet or in a modern data catalog, so they can later be linked to data domains and assets.

2. Run a risk-to-data mapping workshop

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Next, hold a joint workshop with risk, compliance, data governance, and representative domain owners. The goal is to walk through each priority risk and answer three questions.

For each risk:

• Which business capabilities are involved? For example, credit origination, claims management, or customer onboarding.
• Which data domains support those capabilities? For example, customer, product, transaction, or reference data.
• Which systems and data products are actually used? For example, specific dashboards, data marts, or models.

Use simple artifacts like whiteboards or digital canvases, but plan to record the final mapping in a catalog or governance platform so it can be maintained as things change, using a structured data governance framework.

3. Example risk-to-data mapping table

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Convert the workshop outputs into a structured risk-to-data mapping. This provides a shared view for ERM, audit, and data teams. Frameworks like the EDM Council’s Cloud Data Management Capabilities (CDMC) can be helpful for scoping controls and capabilities.

Below is an illustrative table with more than eight rows to show the level of detail to aim for:

Risk ID	Enterprise risk description	Impacted process / decision	Key reports / models	Critical data domains	Critical data elements	Primary systems / platforms	Regulatory driver(s)
R1	Misstated regulatory capital	Capital adequacy calculation	Capital ratio model; regulatory returns	Finance; risk; reference data	exposure at default; LGD; PD; risk weights	Data warehouse; risk engine; reporting mart	BCBS 239
R2	Mispriced credit products	Credit pricing & approval	Pricing models; approval workflows	Customer; credit; product	credit score; income; pricing curves	Loan origination system; model platform	ISO 31000
R3	AML / sanctions breach	Transaction monitoring	AML rules engine; screening tools	Customer; transactions; counterparties	beneficiary name; country; transaction amount	Core banking; AML platform	NIST SP 800-37
R4	Privacy / data protection breach	Customer data handling	DPIA; privacy risk reports	Customer; consent; marketing	email; phone; consent flags	CRM; marketing cloud; data lake	ICO Accountability Framework
R5	Misstated financial results	Financial close & reporting	P&L; balance sheet; management reports	GL; reference; product	chart of accounts; FX rates	ERP; consolidation tool; BI reports	COSO ERM
R6	AI model bias and unfair outcomes	AI decisioning	Model output dashboards; fairness reports	Customer; behavioral; model features	protected attributes; feature importance	Feature store; ML platform	ISO 31000
R7	Operational outage of critical reports	Reporting & analytics	Executive KPI dashboards	KPI catalog; operational metrics	uptime; incident count; MTTR	BI platform; incident tool	NIST SP 800-37
R8	Data exfiltration or insider abuse	Access management	Access review reports; DLP dashboards	Security; access logs	user ID; role; access grants	IAM; data warehouse; DLP tool	NIST SP 800-37
R9	Third-party data quality failures	Vendor data ingestion	Vendor scorecards; quality reports	Vendor; market data	vendor ID; reference prices	Vendor feeds; data lake	DAMA-DMBOK

This table becomes a key bridge between ERM and data governance. It guides control design, monitoring, and investment decisions.

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Design risk-based data governance controls and policies

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Once risk-to-data mappings are clear, the next step is to define controls and policies that explicitly mitigate those risks. This is where traditional data governance artifacts (standards, glossaries, classification schemes) meet risk concepts like control objectives, control types, and assurance levels, as described in COSO ERM.

Using a control library grounded in data governance best practices and frameworks like DAMA-DMBOK and the EDM Council’s CDMC framework helps ensure coverage without reinventing everything from scratch.

1. Translate risks into control objectives

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For each material risk, define clear control objectives that describe what must be true about data, systems, and processes. Good control objectives are:

• Specific to the risk. For example, “Regulatory capital reports are complete, accurate, and reconcilable to the general ledger.”
• Measurable via data quality rules, lineage completeness, or access logs.
• Assignable to accountable owners, both business and technical.

Where possible, align terminology with your existing control frameworks, such as SOX, NIST, or sector standards. This simplifies conversations with audit and regulators. NIST SP 800-37

2. Build a reusable data control library

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Instead of designing controls per project, create a reusable library of standard data controls. Map each control to: risk types, control types (preventive/detective/corrective), and data domains.

An example extract with 15 controls:

Control ID	Control name	Control type	Description	Primary risk(s) addressed	Data scope
DC-01	Critical data element (CDE) inventory	Preventive	Maintain approved list of CDEs for each risk and domain, with definitions and owners.	Misstatement; regulatory non-compliance	Finance; risk; customer
DC-02	Lineage for regulatory reports	Preventive	Maintain end-to-end lineage from regulatory reports back to source systems.	Regulatory reporting failure	Finance; risk
DC-03	Data quality rules on CDEs	Detective	Implement rules for completeness, validity, and reconciliation on CDEs.	Misstatement; model risk	All critical domains
DC-04	Break resolution workflow	Corrective	Route data quality breaks to owners with SLA, approvals, and evidence tracking.	Operational risk; reporting gaps	All
DC-05	Access control by sensitivity	Preventive	Enforce role-based access and masking based on classification.	Privacy; insider abuse	Customer; HR; transactions
DC-06	Periodic access reviews	Detective	Quarterly review of privileged and high-risk access with sign-off.	Privacy; data exfiltration	All sensitive domains
DC-07	Policy-based retention and deletion	Preventive	Apply retention schedules and automated deletion jobs per policy.	Privacy; legal holds	Customer; logs; content
DC-08	Model input and output logging	Detective	Log inputs, outputs, and versions for high-risk models.	Model risk; conduct risk	AI / ML
DC-09	Reference data governance	Preventive	Approve and control changes to reference and pricing data.	Mispricing; P&L errors	Reference; market data
DC-10	Vendor data onboarding checks	Preventive	Validate vendor data completeness, format, and contracts before production use.	Third-party risk	Vendor data
DC-11	KPI catalog and certification	Preventive	Maintain a catalog of KPIs with definitions, owners, and certification workflows.	Misleading reporting	Enterprise KPIs
DC-12	Change impact assessment	Preventive	Assess data and risk impact before schema or pipeline changes.	Operational outages	All
DC-13	Incident and breach logging	Detective	Centralized logging of data incidents with root cause and remediation.	Security; privacy	All
DC-14	Encryption and tokenization	Preventive	Encrypt data at rest and in transit; tokenize high-risk fields.	Security; privacy	Sensitive data
DC-15	Data classification automation	Preventive	Automatically classify PII, PCI, PHI, and critical fields using patterns and metadata.	Privacy; model drift	All

Active metadata platforms such as Atlan can host this control library, link controls to specific assets, and embed them into workflows like schema changes or access approvals, as described in Atlan’s data governance framework and active data governance. The Atlan Blueprint provides templates and assessments to stand up this kind of control library efficiently.

3. Policy-as-controls specification example

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To move from ‘policy PDFs’ to enforceable governance, express policies as structured control specifications. This makes them testable, automatable, and auditable, in the spirit of capability models like CDMC.

Below is an example for a regulatory capital data quality policy, specified as ‘policy-as-controls’:

policy_id: P-REGCAP-DQ-001
policy_name: Regulatory capital data quality
risk_id: R1
risk_statement: Misstated regulatory capital due to incomplete or inaccurate risk exposure data.
scope:
  domains: [finance, risk]
  systems: [risk_engine, data_warehouse, reporting_mart]
  critical_data_elements:
    - exposure_at_default
    - loss_given_default
    - probability_of_default
control_objectives:
  - CO-1: All CDEs required for regulatory capital reports are present and complete.
  - CO-2: Aggregated exposure values reconcile to the general ledger within defined thresholds.
controls:
  - control_id: DC-03
    type: detective
    description: Data quality rules for completeness and reconciliation on RWA CDEs.
    frequency: daily
    thresholds:
      completeness: 100%
      reconciliation_diff: <= 0.1%
  - control_id: DC-04
    type: corrective
    description: Breaks routed to data owner with 2-business-day SLA.
    workflow_system: governance_platform
monitoring:
  kpis:
    - name: reg_cap_dq_pass_rate
      target: '>= 99.5%'
    - name: dq_issue_mttr_days
      target: '<= 2'
evidence:
  - dq_rule_results
  - reconciliation_reports
  - workflow_logs
owner:
  business_owner: Head of Regulatory Reporting
  data_owner: Finance Data Owner
review:
  frequency: annual
  approvers:
    - CRO
    - CDO

This structured approach enables governance policies to be ingested into governance platforms, audited, and evolved without manual re-documentation.

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Coming up: Operationalizing this framework day-to-day, measuring it, and evolving it as the organization and risks change.