PCI DSS Compliance

Encryption, Hashing, and Tokenization



Per every Experian industry forecast in the last five years, the number of data breaches will continue to rise. Ponemon Institute studies of data breaches reveal that the average cost to a US organization exceeds $200 for each compromised customer record. With an average of 29,000 records compromised per incident, the cost of a data breach in this country can reach well over $5 million. In 2018, the global average was $3.86M per breach, or $148 per record, increases of 6.4 and 4.8%, respectively.


In addition to the significant financial harm that results from a data breach, there is an acute loss of trust between an organization and its customers. Both the breach and the fallout are usually well publicized and long remembered.


According to this SecurityMetrics analysis of Payment Card Industry (PCI) Data Breaches in 2018, despite the fact that 12 documented PCI Data Security Standard (DSS) requirements were largely in place, external (50%), internal (33%) breaches still continued.


To help mitigate or even nullify the effects of data breaches, and help BFSI companies and other organizations managing credit card data comply with PCI DSS rules, the data discovery and masking functions in IRI Data Protector Suite products -- or the IRI Voracity platform -- find and protect primary account number (PAN), or credit card number values (along with other data at risk) in multiple data sources.

The applicable field-level security functions are strong encryption, SHA-2 cryptographic hashing, and tokenization.

For example in structured data sources like normal form relational database columns and fields in flat files, IRI FieldShield users apply their choice of data classification and protection functions to PANs and other columns in an intuitive, efficient, and flexible manner under Eclipse. For example, specification of an encryption cipher with a pass-phrase occurs in a simple dialog:

Here, format-preserving encryption is used to comply with PCI, to ensure that no changes are required to the table or database structure, and to possibly deceive hackers into thinking they have actual PANs.

These easy, yet powerful functions can also help you limit the financial and operational impact of a data breach. For example, Steam, a gaming distribution platform, suffered a data breach. As significant as the breach was, the overall impact to Steam was limited because the credit card values were encrypted.


FieldShield and the other IRI data masking products (DarkShield for multiple forms of semi- and unstructured data, and CellShield for Excel spreadsheets) -- which have common data classification, scanning, and masking functions -- provide simplicity, affordability, and peace-of-mind by finding and securing credit card data and other PII at rest. They help organizations like this one meet PCI DSS v4 requirements for protecting stored cardholder data, while mitigating the risk of data loss and providing safe, intelligent test data targets.


It is also possible to encrypt/decrypt or redact PANs or PII in a dynamic data masking context, through an application that queries a database, for example.