FieldShield (Data Masking)

PII Data Privacy

Data Masking Solutions

Guide for data masking tools

Find and mask sensitive data
Classify PII centrally, search for it globally, and mask it automatically. Maintain realism, referential integrity, and/or reversibility through field-level encryption, pseudonymization, synthesis, and other shareable rules for production and test environments. Utilize RBACs, custom business logic, DQ, and ETL functions, etc. More..

Compliance with data protection laws
De-identification, deletion, provisioning, or anonymization of data subject to GDPR, DPDPA, HIPAA, PCI, POPI, PIPEDA, PCI-DSS, SOC2, etc., using your on-premises or cloud hardware. Verify compliance through human and machine-readable search reports and job audit logs. and Re-identification risk assessments. More..

Data Protection Throughout Its Lifecycle
Optionally mask data as you map it. Apply FieldShield functions in IRI Voracity ETL, federation, migration, replication, subsetting, or analysis jobs. Run FieldShield from Actifio, Commvault, or Windocks to mask DB clones. Use deterministic masking functions consistently to maintain data integrity across your schema and beyond. More..

References

FieldShield Use Cases

Credit Card Transactions – PCI

„FieldShield encrypts and decrypts fields in our credit card migration and testing sources, and easily generates and manages encryption keys.“

More details.

Health Data – PHI

„We continue to rely on FieldShield for the de-identification of flat files and databases to comply with state healthcare privacy regulations.“

More details.

Personally Identifiable Information – PII

„We use FieldShield to anonymize HR data in complex file feeds and to segment and replace values based on field-level conditions.“

More details.

Do you need FieldShield? Find out!

Do you capture or process personal data or other „sensitive data“?
Do you know where these (all) are located?
Are these data secure from a breach, meaning could they be used in the event of theft or disclosure?
Does your department comply with data protection regulations?
Can you prove this?
Use multiple tools or methods to protect different DB columns in different ways?
Do you need to mask data in different databases and file formats in the same way?
Can you protect only the sensitive data, so that you can see and use the non-sensitive data?
Do your masked data look real enough?
Are they deterministic (referentially correct)?
Can you check your masked datasets for re-identification risk and anonymize quasi-identifiers?
Is it taking too long to learn, implement, modify, or optimize your data masking jobs?
Can you mask data in your ETL, subsetting, migration/replication, CDC, or reporting tasks?
See our customers here