Data Masking/Protection
IRI DarkShield data masking software focuses on discovering and masking sensitive data in a wide variety of sources, including unstructured and semi-structured files, documents, and databases. Read More
Data Masking/Protection
In a previous article, we detailed a method for securing the encryption keys (passphrases) used in IRI FieldShield data masking jobs through the Azure Key Vault. Read More
Data Masking/Protection
One of the primary uses of IRI FieldShield is to encrypt and decrypt sensitive data in database or flat-file columns. FieldShield relies on a passphrase to derive a symmetrical encryption key used at encryption and decryption time. Read More
Data Masking/Protection
Usually static data masking is performed on production data at rest so it is stored safely, or when replicated to non-production environments for testing or development purposes. Read More
Big Data
IRI provides a software development kit (SDK) to help FieldShield users apply column-level encryption, decryption, hashing, and redaction algorithms in Java and .NET projects for more in-situ or dynamic data masking requirements. Read More
Data Masking/Protection
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. Read More
Data Masking/Protection
According to Trent Henry of the Burton Group,
Data protection needs to take into account both internal and external threats, including those against critical database and application resources. Read More
Data Masking/Protection
Summary
The protection of sensitive information and data loss prevention (DLP) are critical elements of modern data governance. IRI’s FieldShield is a uniquely versatile tool for securing sensitive fields in database tables and flat files with a choice of protections that operate according to business rules to de-identify or remove individualizing characteristics, without cutting off access to the rest of the data or changing their basic appearance. Read More