AI data governance and compliance framework for enterprise AI
CEI implemented a scalable data governance framework to enable compliant, transparent, and secure AI development across enterprise data environments.
Centralized data visibility
Improved control across data assets
Automated data classification
Enhanced detection of sensitive info
End-to-end data lineage
Improved accountability and compliance
Company Overview
A federally chartered financial institution supports housing and community development by providing funding and related services to member organizations. The institution manages multiple programs, including affordable housing initiatives that require frequent interaction with members and stakeholders.
The Challenge
Regulatory complexity
Need to comply with GDPR, HIPAA, and emerging AI regulations
Limited data visibility
Lack of centralized view into data assets used in AI models
Governance gaps
Inconsistent classification and access control across datasets
Risk exposure
Potential compliance and security risks in AI development lifecycle
The Solution
CEI implemented an enterprise data governance and security framework using Microsoft Purview to provide visibility, control, and compliance across data assets used in AI systems. The framework enables automated classification, metadata management, lineage tracking, and role-based access control, ensuring governance is embedded throughout the data lifecycle—from ingestion to model deployment.
Centralized data catalog and visibility across assets
Automated detection and classification of sensitive data
End-to-end data lineage and access tracking
Role-based access control aligned with governance policies
Compliance support for regulatory and audit requirements
The Results
Centralized visibility
Improved understanding and control of enterprise data assets
Automated classification
Enhanced identification of sensitive and regulated data
End-to-end lineage tracking
Increased accountability across data usage and access
Additional outcomes include improved compliance with regulatory standards, stronger audit readiness, and better transparency across the AI data lifecycle.
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