Enterprise Data Governance Policy

Version: 1.0
Effective Date: [Insert Date]
Last Reviewed: [Insert Date]
Approved By: Chief Data Officer (CDO) and Chief Compliance Officer (CCO)
Applies To: All employees, contractors, vendors, and third parties handling company data across all global operations.

Table of Contents

1. Purpose
2. Scope
3. Definitions
4. Governance Structure and Roles
5. Data Classification
6. Cross-Departmental Data Ownership
7. Data Lifecycle Management
8. AI-Generated Content Classification and Controls
9. Compliance with GDPR
10. Compliance with SOC 2
11. Data Access, Usage, and Security
12. Data Quality and Metadata Management
13. Monitoring, Auditing, and Reporting
14. Training and Awareness
15. Enforcement and Exceptions
16. Review and Updates
17. Appendices

1. Purpose

This policy establishes a framework for managing enterprise data as a strategic asset, ensuring data quality, integrity, security, and compliance. It promotes ethical use of data, including AI-generated content, while mitigating risks associated with data ownership across departments and adherence to global regulations such as GDPR and SOC 2. The policy aligns with the company's mission to deliver secure, reliable SaaS solutions to multinational customers.

2. Scope

This policy applies to all data assets owned, processed, or stored by the company, including customer data, internal operational data, and AI-generated outputs. It covers all business units, departments, subsidiaries, and third-party integrations globally. Exclusions: De minimis personal data not processed by systems (e.g., business cards).

3. Definitions

• Data Asset: Any structured or unstructured data, including databases, files, logs, and AI outputs.
• AI-Generated Content: Data or outputs produced wholly or partially by AI/ML models (e.g., synthetic data, summaries, predictions).
• Data Owner: Senior executive accountable for a data domain.
• Data Steward: Operational role responsible for day-to-day data management.
• Personal Data: Information relating to an identified or identifiable natural person (GDPR-defined).
• Sensitive Data: Personal data revealing racial/ethnic origin, health, biometrics, etc., or confidential business information.

4. Governance Structure and Roles

• Data Governance Council (DGC): Chaired by CDO; includes C-level reps from Legal, IT, Security, Compliance, and key business units. Meets quarterly to oversee policy enforcement.
• Data Owners: Assigned per domain (e.g., Customer Data Owner: CRO).
• Data Stewards: Departmental leads reporting to Data Owners.
• Data Custodians: IT/Security teams managing technical storage/access.
• Chief Privacy Officer (CPO)/Data Protection Officer (DPO): Oversees GDPR compliance.

5. Data Classification

Data is classified into four levels based on sensitivity, regulatory impact, and business value:

Level	Description	Examples	Handling Requirements
Public	No confidentiality risk	Marketing collateral	No restrictions
Internal	Low risk if disclosed	Internal memos	Access via company tools
Confidential	Moderate risk (e.g., IP)	Financial reports, customer metadata	Encryption in transit/rest; RBAC
Restricted	High risk (e.g., PII, PHI)	Health data, credentials	Encryption, MFA, audit logs; no AI training without approval

All data must be labeled at creation/storage using metadata tags.

6. Cross-Departmental Data Ownership

• Data Domains: Defined matrix (e.g., Customer, Product, Financial). See Appendix A.
• Ownership Model:

  Domain	Primary Owner	Steward Depts	Shared Access
  Customer	CRO	Sales, Support, Marketing	Via central data lake with approval
  Product	CTO	Engineering, Product Mgmt	Cross-dept API with logging
  Financial	CFO	Finance, HR	Restricted to need-to-know

• Resolution Process: Conflicts escalated to DGC. Stewards collaborate via shared repositories (e.g., data catalog). All cross-dept usage requires Data Owner approval and Data Processing Agreements (DPAs) for internal transfers.
• Federated Stewardship: Each dept appoints stewards; central Data Office coordinates.

7. Data Lifecycle Management

• Create/Acquire: Classify and tag immediately.
• Store: Use approved repositories (e.g., AWS S3 with encryption).
• Use/Share: RBAC; pseudonymize where possible.
• Archive/Dispose: Retain per retention schedules (e.g., 7 years for financial); secure deletion.

8. AI-Generated Content Classification and Controls

• Classification:

  Type	Sub-Type	Classification	Controls
  Fully AI-Generated	Synthetic data, hallucinations	Restricted (if PII-like) or Confidential	Mandatory human review; watermarking/labeling (e.g., "AI-GEN"); no customer-facing without validation
  AI-Assisted	Summaries, predictions	Internal/Confidential	Metadata tag "AI-ASSIST"; bias audits; retraining logs
  AI-Trained	Model inputs/outputs	Restricted	Anonymization required; DPIA for high-risk

• Requirements:
  • All AI tools registered with Data Office.
  • Outputs classified per Section 5; prohibit training on Restricted data without DGC approval.
  • Human-in-the-loop for customer-impacting AI (e.g., chatbots).
  • Auditing: Track provenance, model version, confidence scores.

9. Compliance with GDPR

• Principles: Lawfulness, fairness, transparency, purpose limitation, minimization, accuracy, retention, integrity/confidentiality.
• Controls:
  • DPIAs for high-risk processing (e.g., AI profiling).
  • Data subject rights (access, rectification, erasure) processed within 30 days via privacy@company.com.
  • Consent management; legitimate interest assessments.
  • International transfers: SCCs, BCRs, or adequacy decisions.
  • DPO reports to senior management; annual privacy audits.
  • Breach notification: 72 hours to supervisory authorities.

10. Compliance with SOC 2

• Trust Criteria:

  Criterion	Key Controls
  Security	CC6/CC7: Logical/physical access; encryption; vulnerability scans.
  Availability	CC9: Backup/DRP; 99.9% uptime SLAs.
  Processing Integrity	CC3: Input/output validation; AI accuracy checks.
  Confidentiality	CC6: Data masking; secure APIs.
  Privacy	CC2: Notice/consent; GDPR-aligned.

• Annual SOC 2 Type II audits by independent auditor; bridge letters for customers.

11. Data Access, Usage, and Security

• Role-Based Access Control (RBAC) enforced via IAM (e.g., Okta).
• Least privilege; zero-trust model.
• Prohibit shadow IT; all tools via approved catalog.

12. Data Quality and Metadata Management

• Quality Dimensions: Accuracy, completeness, timeliness, consistency.
• Tools: Data catalog (e.g., Collibra) for lineage/metadata.
• Quarterly quality audits by Stewards.

13. Monitoring, Auditing, and Reporting

• Automated monitoring (e.g., SIEM for anomalies).
• Annual audits; incident reporting within 24 hours.
• DGC dashboards for KPIs (e.g., classification compliance >95%).

14. Training and Awareness

• Mandatory annual training; role-specific modules on AI, GDPR/SOC 2.
• Phishing simulations; AI ethics workshops.

15. Enforcement and Exceptions

• Violations: Progressive discipline up to termination.
• Exceptions: Submit to DGC; approved in writing, time-bound.

16. Review and Updates

• Annual review by DGC; updates for regulatory changes.

17. Appendices

• A: Data Domain Matrix.
• B: Retention Schedule.
• C: AI Tool Registration Form.
• D: Contact List (DPO: dpo@company.com).

Acknowledgment: All personnel must acknowledge receipt annually via HR portal.

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This policy is confidential and proprietary. Distribution requires approval.

Enterprise Data Governance Policy

1. Purpose and Scope

This Data Governance Policy establishes guidelines for the effective management, protection, and responsible use of data across [Company Name], a multinational SaaS provider. It ensures data is accurate, secure, compliant, and trusted to support strategic decision-making, AI initiatives, and operational efficiency. The policy applies to all employees, contractors, vendors, and authorized individuals who access, handle, or manage company data worldwide, including customer, financial, operational, product, and AI-generated data assets in production systems, databases, and AI/ML pipelines. Exclusions include publicly available non-proprietary data.[1][2][3][5][6]

2. Governance Mission and Objectives

The mission is to align data practices with business priorities, ensuring high-quality data drives revenue growth, reduces risks, and enables compliant AI deployment. Key objectives include:

• Achieving 95% data quality compliance (accuracy, completeness, timeliness).
• Ensuring full adherence to GDPR and SOC 2.
• Reducing duplicate records by 50% and access violations by 80%.
• Enabling secure cross-departmental data sharing for AI models.[1][2][3]

3. Roles and Responsibilities

Clear ownership prevents ambiguity, especially for cross-departmental data and AI content. A Data Governance Council (chaired by the Chief Data Officer or CIO) provides oversight, approves policies, and resolves disputes. Roles follow a RACI (Responsible, Accountable, Consulted, Informed) model:

Role	Key Responsibilities	Cross-Departmental Focus	RACI Examples for AI Data Ownership
Data Owners (Department heads, e.g., Sales, Engineering)	Define business rules, approve access, ensure quality; accountable for domain data.	Assign joint ownership for shared datasets (e.g., customer data used in Marketing and Product AI).	Accountable for classifying AI-generated content in their domain. [1][2][7]
Data Stewards (Analysts per department)	Maintain definitions, monitor quality, handle metadata; report issues to owners.	Coordinate with other departments on shared data lineage.	Responsible for tagging AI-generated vs. human-curated data. [1][2]
Data Custodians (IT/Security teams)	Implement technical controls (encryption, access); manage storage and backups.	Enforce centralized access for multinational ops.	Informed on AI classification for tooling integration. [1][5]
Data Governance Council	Oversight, policy approval, audits; includes reps from Legal, Compliance, all depts.	Arbitrate ownership disputes across borders/depts.	Consulted/Approves AI content policies. [1][3]
AI Ethics Officer (New role under Council)	Review AI-generated content for bias, accuracy; classify per policy.	Cross-dept collaboration on AI data flows.	Accountable for AI-specific classifications. [Inference from 1,2]
Compliance Officer	Ensure GDPR/SOC 2 alignment; conduct audits.	Global enforcement.	Informed on all classifications. [1][4]

Department heads must map stakeholders and data domains annually. Training is mandatory for all roles.[1][2][3][6]

4. Data Classification

Data is classified by sensitivity, criticality, regulatory impact, and origin to determine handling rules. Classifications drive access, retention, and protections.

Classification Level	Description	Examples (incl. AI-Specific)	Handling Requirements
Public	No sensitivity; low risk.	Marketing collateral; anonymized aggregates.	No restrictions.
Internal	Business-sensitive; limited risk.	Operational metrics; non-PII AI model outputs.	Role-based access.
Confidential	High business value; moderate risk.	Financials; customer non-PII; AI-generated synthetic data.	Encryption at rest/transit; audit logs.
Restricted	PII/high risk; legal penalties.	GDPR-covered personal data; AI-generated content mimicking PII.	Need-to-know; DPO approval; pseudonymization. [1][3][5]

AI-Generated Content Classification: All AI outputs (e.g., from LLMs, generative models) must be tagged as "AI-Generated" metadata field upon creation. Stewards classify based on:

• Origin: Model used, prompts, training data lineage.
• Risk: Hallucinations, bias, or PII leakage trigger "Restricted".
• Automation: Tools auto-flag via metadata scanners; manual review for high-risk. Unclassified AI content cannot be used in production or shared externally.[1][2][Inference: Extends classification to AI per query needs]

5. Cross-Departmental Data Ownership

• Joint Ownership Model: Shared datasets (e.g., customer profiles used in Sales, Support, AI Product) require a lead owner (primary department) and co-owners (via RACI). Council assigns via annual data inventory.[1][7]
• Data Lineage Tracking: Mandatory for cross-dept flows, including AI pipelines, using tools for provenance (e.g., from source to AI output).[1]
• Escalation Process: Disputes resolved by Council within 5 business days; no data use until resolved.
• Workflow Integration: Embed approvals in tools (CRM, data lakes) for seamless sharing.[1]

6. Compliance with GDPR and SOC 2

Policies align with regulations via standardized controls.

GDPR Compliance

• Lawful Basis: Process PII only with consent, contract, or legitimate interest; document in DPIAs.
• Data Subject Rights: Support access, rectification, erasure (right to be forgotten) within 30 days; automate via self-service portals.
• Cross-Border: Standard Contractual Clauses (SCCs) for non-EU transfers; Binding Corporate Rules (BCRs) for intra-company.
• DPO Oversight: Data Protection Officer reviews all PII classifications and AI uses.[1][3][4]

SOC 2 Compliance

• Trust Criteria:

  Criterion	Controls Implemented
  Security	Encryption (AES-256), MFA, vulnerability scans; AI content encrypted if Restricted.
  Availability	99.9% uptime SLAs; redundant storage for critical/AI data.
  Processing Integrity	Quality metrics (<2% invalid records); AI outputs validated pre-use.
  Confidentiality	Access logs (1-year retention); no unauthorized AI training on confidential data.
  Privacy	GDPR-aligned notices; third-party audits.

• Annual SOC 2 Type II audits; breach notification within 72 hours (GDPR).[1][4]

7. Data Quality, Access, and Security Standards

• Quality Rules: Accuracy (>98%), completeness (>95%), consistency; metrics monitored via dashboards (e.g., % incomplete records).[1]
• Access: Need-to-know, least privilege; RBAC with annual reviews. AI access requires steward approval.[5]
• Retention: Per classification (e.g., Restricted: min 7 years post-relationship); auto-deletion.[1][5]
• Security: Encryption, anonymization for AI; prohibit unapproved shadow AI.[5]
• Sharing/Integration: Promote via secure catalogs; no ad-hoc exports.[4]

8. Processes and Workflows

• Data Inventory: Annual mapping of domains, owners, classifications.
• Issue Resolution: SLA <48 hours; escalate to Council.
• Technology: Use metadata tools, quality scanners, lineage platforms integrated into workflows.[1]
• Metrics: Track via real-time dashboards (e.g., access violations, quality scores).[1]

9. Monitoring, Training, and Enforcement

• Audits: Quarterly by Compliance; annual maturity assessments.[4]
• Training: Annual mandatory sessions; role-specific modules on AI classification.
• Enforcement: Violations trigger warnings, access suspension, or termination; tied to performance reviews.[1][3]
• Review: Policy reviewed annually or post-incident by Council.[2]

Approved by: Data Governance Council and Executive Leadership. Effective Date: [Insert Date].