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EKG Catalog
Data Privacy & Governance
EKGF/ekg-catalog

Data Privacy & Governance

Data Classification & Criticality Data Protection Data Privacy & Governance Data Flow & Lineage Mapping
Data Classification & Criticality Data Protection Data Privacy & Governance Data Flow & Lineage Mapping
New to this diagram? Learn about the method: Use Case Tree · Use Case · Outcome

Data privacy and governance ensures compliance with privacy regulations by tracking personal data across all systems, understanding data lineage from collection to deletion, managing consent and sharing agreements, and responding to data subject requests. This use case addresses automatic lineage discovery, cross-system visibility of data flows, automated compliance reporting, relationship discovery to identify privacy risks, and demonstrating compliance through automated generation of responses to data subject access, rectification, erasure, and portability requests.

The Challenge

Privacy regulations require organizations to:

  • Track personal data across all systems and applications
  • Understand data lineage from collection to deletion
  • Manage consent and sharing agreements
  • Identify and respond to data subject requests
  • Demonstrate compliance through automated reporting
  • Monitor cross-system data flows and relationships

Traditional approaches cannot efficiently model or query these complex relationships across siloed systems.

Why EKG is Required

Privacy regulations require understanding data lineage, consent, sharing agreements, and cross-system relationships. EKG provides:

  • Semantic data modeling — Ontologies enable precise modeling of personal data, consent, and privacy relationships
  • Automatic lineage tracking — Graph structure enables automatic discovery and tracking of data flows
  • Cross-system visibility — Semantic relationships enable queries across all systems, not just isolated views
  • Automated compliance — Graph queries enable automated generation of compliance reports and responses to data subject requests
  • Relationship discovery — Graph algorithms can discover unexpected data relationships and privacy risks

Business Value

  • Reduced regulatory fines — Automated compliance reduces risk of violations
  • Improved customer trust — Transparent privacy management builds trust
  • Automated compliance reporting — Reduces manual effort and improves accuracy
  • Faster response to data subject requests — Automated queries enable rapid responses
  • Proactive risk management — Early identification of privacy risks