{"id":18497,"date":"2025-07-30T16:25:24","date_gmt":"2025-07-30T20:25:24","guid":{"rendered":"https:\/\/www.iri.com\/blog\/?p=18497"},"modified":"2026-03-31T14:30:29","modified_gmt":"2026-03-31T18:30:29","slug":"ai-data-classification-for-rdbs","status":"publish","type":"post","link":"https:\/\/www.iri.com\/blog\/data-protection\/ai-data-classification-for-rdbs\/","title":{"rendered":"AI Data Classification for RDBs"},"content":{"rendered":"
Introduction<\/b>
\n<\/b><\/b><\/h5>\n

Data Classification<\/span><\/a> in the <\/span>IRI Workbench<\/span><\/a> GUI is used to name and find specific kinds of data \u2013 usually Personally Identifiable Information (PII) \u2013 and to consistently apply chosen functions (e.g., pseudonymization) to each data class. Applying deterministic masking functions to data classes preserves referential integrity in <\/span>IRI FieldShield<\/span><\/a> and <\/span>DarkShield<\/span><\/a> data masking results.<\/span><\/p>\n

Defining and locating data classes \u2013 like names and addresses, ID, phone, and credit card numbers can be time-consuming, however, in a Relational Database (RDB) with hundreds of tables and thousands of columns. New IRI AI functionality can now accelerate this process.\u00a0<\/span><\/p>\n

The new \u201c<\/span>AI Schema Data Classification<\/span><\/i>\u201d wizard can scan relational schemas to automate both the identification of data classes and the specification of default masking rules for those classes. The results of the wizard allow FieldShield and DarkShield (or <\/span>Voracity<\/span><\/a> platform) users to build and run RDB data discovery and de-identification jobs<\/span> <\/span>1<\/sup><\/a><\/span> <\/span>much sooner than they could before.<\/span><\/p>\n

This additional RDB data discovery capability is yet another application of AI in IRI data masking tools. DarkShield, for example, already leverages AI to discover and mask names in unstructured text through Named Entity Recognition (<\/span>NER<\/span><\/a>) models, as well as <\/span>signatures <\/span><\/a>within documents. IRI will also use AI to protect faces, PII in handwriting, and complex address presentations.<\/span><\/p>\n

Where Is It?<\/b><\/h5>\n

The new <\/span>AI Schema Data Classification<\/span><\/i> wizard is located directly inside the Data Classes and Rules (iriLibrary.dcrlib) file dialog in IRI Workbench. In the top toolbar, located next to the Data Class creation button, there is a new<\/span> Robot icon that is used to denote the new AI scanning tool:<\/span><\/p>\n

\u00a0\"\"<\/i><\/b><\/p>\n

How Is It Helpful?<\/b><\/h5>\n

The wizard helps database users set up data classes faster. Regardless of whether the default rule attached works in your situation, at the very least, the manual work of creating the data class and its <\/span>location matcher<\/span><\/a> will already be completed for you.<\/span><\/p>\n

Using various combinations of detection methods \u2013 including pattern matching, context, custom logic, and checksums \u2013 the current AI model searches for these 29 classes of data and assigns each the default <\/span>masking rule<\/span><\/a> shown here:<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Data Class Name<\/b><\/td>\nDescription<\/b><\/td>\nDefault Masking Rule\u00a0<\/b><\/td>\n<\/tr>\n
Phone Number<\/span><\/td>\nTelephone number (multiple formats)<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
IP Address<\/span><\/td>\nIPv4 or IPv6 Internal Protocol address<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Email Address<\/span><\/td>\nRFC-822 validated email address<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Location<\/span><\/td>\nCity, province, region, country, ocean\u00a0<\/span><\/td>\nPseudonymization<\/span><\/td>\n<\/tr>\n
Person<\/span><\/td>\nFirst, middle, last or full name<\/span><\/td>\nPseudonymization<\/span><\/td>\n<\/tr>\n
NRP<\/span><\/td>\nNationality, religious or political group<\/span><\/td>\nPseudonymization<\/span><\/td>\n<\/tr>\n
Credit Card<\/span><\/td>\n12-19 digit credit card #<\/span><\/td>\nSynthesis<\/span><\/td>\n<\/tr>\n
Date\/Time<\/span><\/td>\nDates and time periods < 1 day<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Crypto<\/span><\/td>\nCryptocurrency (Bitcoin) wallet number<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Medical License<\/span><\/td>\nCommon medical license numbers<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
IBAN Code<\/span><\/td>\nInternational Bank Account Number<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
URL<\/span><\/td>\nUniform Resource Locator (web address)<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Australia Business\u00a0<\/span><\/td>\n11-digit ID for Australian Business Register companies<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Australia Company\u00a0\u00a0<\/span><\/td>\n9-digit ID for Commonwealth Corporations Act companies<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Australia Medicare\u00a0<\/span><\/td>\nCardholder ID for medical expense rebates<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Australia TFN<\/span><\/td>\nTax file number (ATO ID) for every taxpaying entity<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
India PAN<\/span><\/td>\n12-digit Permanent Account # (tax ID)<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
India Aadhaar<\/span><\/td>\n12-digit individual identity #<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
India Vehicle\u00a0\u00a0<\/span><\/td>\ngovernment-issued transport registration #<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
India Voter ID\u00a0<\/span><\/td>\n10-digit alphanumeric # for each citizen<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
India Passport\u00a0\u00a0<\/span><\/td>\nIndian passport #<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
Singapore NRIC<\/span><\/td>\nNational Registration Identification Card #<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
UK NHS\u00a0<\/span><\/td>\n10-digit National Health System ID<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
UK NINO<\/span><\/td>\nNational Insurance Number (also used for taxation)<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
US Bank Number<\/span><\/td>\n8-17 digit bank account #<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
US Driver License<\/span><\/td>\nState driver license # in <\/span>NTSI<\/span><\/a> format<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
US ITIN<\/span><\/td>\n9-digit Individual Taxpayer Identification Number<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
US Passport<\/span><\/td>\n9-digit passport #<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n
US SSN<\/span><\/td>\n9-digit Social Security Number<\/span><\/td>\nFPE<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

FPE<\/span><\/a> means Format Preserving Encryption. <\/span>Pseudonymization<\/span><\/a> replaces the value with a realistic, non-reversible substitute. <\/span>Synthesis<\/span><\/a> generates a new PAN in the same format.<\/span><\/p>\n

Although you cannot currently add classes to this scanning model, you can define additional data classes, and rules for those (as well as modify the rules for the classes above) \u2026 whatever best suits your requirements. See <\/span>this article<\/span> on the Data Class and Rule Library for details on how to configure, group, and prioritize data class<\/span><\/a>es.<\/span><\/p>\n

Once your data class rules are defined, you can configure RDB data discovery and masking jobs. For DarkShield, use the <\/span>New Relational DB Search\/Masking Job<\/span><\/i> wizard per <\/span>this article<\/span><\/a>. For FieldShield, see: 1) <\/span>this article<\/span><\/a> on the <\/span>Schema Data Class Search<\/span><\/i> wizard; 2) <\/span>this article<\/span><\/a> on the <\/span>Data Class Map<\/span><\/i>; and then, 3) <\/span>this article<\/span><\/a> on the <\/span>New Data Class Map DB Masking<\/span><\/i> wizard.<\/span><\/p>\n

Using the Wizard<\/b><\/h5>\n
Prerequisites<\/i><\/b><\/i><\/h6>\n