IRI CellShield® is an add-in for Microsoft Excel® that protects the sensitive information in one or more spreadsheets, allowing you to comply with data privacy laws and nullify data breaches.
Forty years after the first release of IRI CoSort for CP/M, CoSort v10 for Unix, Linux, and Windows platforms was released in the middle of 2018.
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.
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.
In Q3’19, IRI created a new, free app for Splunk Enterprise and Enterprise Security users that seamlessly indexes data from any Voracity platform data wrangling or protection job.
KNIME is a leading open source analytic and visualization tool for data scientists. Wrangling raw data for KNIME projects is usually done via their intermediate file node, database connectors, or other extensions like Spark.
Article 17 of the General Data Protection Regulation (GDPR) stipulates the Right to Erasure, often referred to as the Right to be Forgotten. While the regulation specifies some requirements as to what controllers must do with data requested to be “erased”, it does not expressly define what the term erasure means.
An operational data store (or “ODS”) is another paradigm for integrating enterprise data that is relatively simpler than a data warehouse (DW).
“Have you stopped speeding?” You could probably object to a leading question like this in court, but what happens when an important question with only a yes or no answer is solicited on a mandatory form, and the response becomes part of an actionable database record?
Quasi-identifiers, or indirect identifiers, are personal attributes that are true about, but not necessarily unique, to an individual. Examples are one’s age or date of birth, race, salary, educational attainment, occupation, marital status and zip code.
This is part 4 of a 4-part series on Production Analytics. Processing on Par with Information [Part 1] Data Processing Drives Efficiency [Part 2] Processing Real World Data [Part 3]
In this final article of the series covering the Production Analytic Platform paradigm, we look at data virtualization—a key requirement in today’s multi-source, data-overloaded world.