Editors Note: This example demonstrates our earliest, least direct (though still available) method of using IRI FieldShield to protect data found within MongoDB tables. As you will read, the MongoDB Export Utility in this case extracts data and create a CSV file that FieldShield masks externally, prior to loading the newly secured data back into MongoDB.
Introduction: This example demonstrates an older method of using IRI FieldShield to protect sensitive data prior to indexing the data in Splunk. As you will read, FieldShield would process the data outside of Splunk and create a CSV file for Splunk’s ingestion.
This is an introduction to IRI CellShield. Subsequent blogs will demonstrate the different functions in CellShield: data masking, encryption, and pseudonymization. If you’re interested in finding, classifying, or masking PII in databases or flat files, check out IRI FieldShield.
This article is second in a 3-part series on using IRI products to expand functionality and improve performance in Pentaho systems. We first demonstrate how to improve sorting performance, and then introduce ways to mask production data, and create test data, in the Pentaho Data Integration (PDI) environment.