The document discusses the interactions between data security and data quality. It argues that poor data quality can threaten data security by compromising confidentiality, integrity and availability. Improving data quality through processes like data governance can benefit data security by providing metrics to evaluate effectiveness and enabling security requirements to be implemented properly. The document then presents models for data quality and data security and argues that integrating the two through a unified data governance strategy can help discover vulnerabilities, engineer better security protections and create organizational awareness of both data quality and security issues.

1. Interactions between Data Securityand Data Quality

2. Data Quality and Data Security Models Data Governance integrating Data Quality and Data SecurityAgenda Data Quality Maturity Model and Data Security

3. Data Quality Issues are a Security ThreatConfidentiality, Integrity and Availability Problems as a result of Poor Data Quality Data Ownership is required to control whether confidentiality is maintained.Data Inconsistency measures are required to control if integrity is maintained.Analysis of Data problems hands confidential data to people that shouldn’t have!“ad hoc” Fixing Data problems is in itself a big threat to data integrity!Data Quality Benefitting Data Security in all StagesQualitative Data Governance provides metrics to observe the effectiveness of Data Security.Data Quality processes are required to effectively implement Data Security Requirements.Data Quality Enables Data Security

4. The Data Security ModelIntegrity Modification Tracking

5. Data Accountability

6. Data AuthenticityConfidentiality Data Access Controls

7. Data Encryption

8. Data AnonymizationAvailability Data Access Mechanisms

9. Data Loss Prevention

10. Downtime PreventionThe Data Quality ModelDo the IT systems providehigh quality data fortheir purpose?Do the processes provide data suitableto fit their purpose?Fit ForPurposeMeasureAnalyzeImproveControlMeasureAnalyzeImproveControlDataQualityMgt.Which processesutilize the dataand why?Why do the IT systemshandle the data?Data HandlingIT SystemsData UtilizingProcessesMeasureAnalyzeImproveControlDo the systems providedata suitable to the process?What data handling functiondoes the system providefor the process?

11. Data Quality CharacteristicsThe Data Quality CharacteristicsConsistentCompleteTransparentRelevantTimelyPreciseAccurate

12. The effect of Data Quality on SecurityPoorData QualityDataSecurityThreatsFixingDQProblemsDataSecurityMetricscausesimprovesTimelinessTransparencyConsistencyCompletenessRelevancePrecisionAccuracy

13. Data Quality and Data Security Models Data Governance integrating Data Quality and Data SecurityAgenda Data Quality Maturity Model and Data Security

14. The Data Quality Maturity ModelEnforcementMetadataMethodsPolicyMetadataManagementInformation Lifecycle Mgt.Data Risk ManagementDataArchitectureDataQualityGovernanceMaturityOrganizationalAwarenessValue CreationAudits &ReportingStewardshipSecurityComplianceData QualityMeasurementCorporateEnvironmentManDQControl

15. DQ Governance Benefits for SecurityEnforcementMetadataMethodsCorporateEnvironmentManDQControlOrganizationalAwarenessPolicyValue CreationMetadataManagementInformation Lifecycle Mgt.Audits &Reporting Integrate Data

16. Remove Silos

17. Definition