AI Data Governance

Design data access control models, entitlement frameworks, and least-privilege policies for cloud and on-premises data environments. Reduces insider risk and meets compliance mandates.

Plan and execute enterprise data catalog implementations for discoverability, metadata management, and governance. Covers tool selection, metadata strategy, and adoption.

Design and apply data classification frameworks to categorize sensitive, regulated, and proprietary data. Supports data protection, access control, and compliance programs.

Draft, structure, and refine enterprise data governance policies, standards, and procedures. Audit-ready documentation aligned to regulatory frameworks and industry best practices.

Trace, document, and analyze data lineage across complex pipelines and systems. Supports auditability, compliance, and data trust in AI-driven environments.

Build data quality dimensions, rules, KPIs, and remediation workflows for enterprise data programs. Ensures AI models and analytics operate on trustworthy data.

Design and document data retention schedules and deletion policies aligned to GDPR, HIPAA, and sector-specific regulations. Reduces legal risk and storage costs.

Design data stewardship operating models, role definitions, and accountability structures for enterprise governance programs. Bridges technical data teams and business owners.

Design MDM strategies, data stewardship models, and golden record frameworks for customer, product, and enterprise data. Reduces duplication and improves data trust.

Govern AI training datasets for quality, provenance, bias, consent, and regulatory compliance. Supports responsible AI development and model auditability.