Big Data
This article demonstrates the use of IRI DarkShield to identify and remediate (mask) personally identifiable information (PII) and other sensitive data in MongoDB and Cassandra databases.
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Data Masking/Protection
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.
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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.
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Data Masking/Protection
The Splunk Adaptive Response Framework (ARF) included with Splunk Enterprise Security (ES) allows actions to be taken in response to data. This is done by creating an alert that triggers when a certain search result condition is received in the Splunk ES Search and Reporting app.
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Data Masking/Protection
The Data Class Database Masking Job wizard in IRI Workbench can be used with an IRI FieldShield or Voracity license to mask PII in multiple, disparate database sources that have been previously classified.
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Big Data
Finding and masking personally identifiable information (PII) in Snowflake® data warehouses works the same way in IRI FieldShield® or Voracity® installations as it does for other relational database sources.
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Data Transformation
The Schema Data Class Search wizard in IRI Workbench (WB) can be used to match data in entire schemas to configured data classes. The search process compares the matchers in the data classes with the data to determine the best match, if any.
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Data Masking/Protection
“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?
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Data Masking/Protection
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.
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Data Masking/Protection
Adding ‘random noise’ to data through blurring or perturbation is a data common anonymization requirement for researchers and marketers of protected health information (PHI) seeking to comply with the HIPAA Expert Determination Method security rule.
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