{"id":18509,"date":"2025-07-30T19:54:22","date_gmt":"2025-07-30T23:54:22","guid":{"rendered":"https:\/\/www.iri.com\/blog\/?p=18509"},"modified":"2026-03-04T17:02:13","modified_gmt":"2026-03-04T22:02:13","slug":"fabricating-pii","status":"publish","type":"post","link":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/","title":{"rendered":"Fabricating PII"},"content":{"rendered":"<h5><strong>What is the Identity Fabrication Rule?<\/strong><\/h5>\n<p><span style=\"font-weight: 400;\">The identity fabrication rule is a deterministic but non-reversible <\/span><a href=\"https:\/\/www.iri.com\/solutions\/data-masking\/static-data-masking\"><span style=\"font-weight: 400;\">data masking rule<\/span><\/a><span style=\"font-weight: 400;\"> that can help you create realistic test PII for a whole record from just one original value. The new function, configurable from <\/span><a href=\"https:\/\/www.iri.com\/products\/workbench\"><span style=\"font-weight: 400;\">IRI Workbench<\/span><\/a><span style=\"font-weight: 400;\">, creates meaningful and unique new values by pseudonymizing an initial value (e.g., a name), and synthesizing other values alongside it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The mock data is based on one value in your input data and a seed value. It can be fabricated as a rule in either <\/span><a href=\"https:\/\/www.iri.com\/products\/fieldshield\"><span style=\"font-weight: 400;\">IRI FieldShield<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"https:\/\/www.iri.com\/products\/darkshield\"><span style=\"font-weight: 400;\">IRI DarkShield<\/span><\/a><span style=\"font-weight: 400;\"> data masking jobs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, let&#8217;s say you want to mask the name Tom in every database and file where \u201cTom\u201d appears \u2013 but not just to some random value, but to another realistic-looking name. This is where the Identity Fabrication rule shines.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By passing a name, seed value, and expected return type, you can synthesize a new mock identity for \u201cTom\u201d. That is, beyond just the name replacement for \u201cTom\u201d the rule creates additional, common PII for \u201cTom\u201d as well, which can be used to mask other parts of \u201cTom\u2019s\u201d Personal Identifiable Information (PII).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">With just that one name, the fabrication function will create fake, but realistic PII associated with \u201cTom\u201d. So it will not only replace every instance of Tom with Carl everywhere, but also create a fake Last Name, Phone Number, SSN, and more, just using \u201cTom\u201d as the input source.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Note that you do not need to use any or all of the new mock information. It will be available if you want to use it, but it is not required until you, as a user, ask for it. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Also note that if you need more granular fabrication of PII, there are ways to construct it through a mix of masking, lookup and synthesis functions. See <a href=\"https:\/\/www.iri.com\/blog\/data-protection\/how-to-build-realistic-but-fake-pii\/\">this article<\/a> called &#8216;<em>How to Build Realistic but Fake PII<\/em>&#8216; for an example<\/span><\/p>\n<h5><strong>How is the Identity Fabrication Rule helpful?<\/strong><\/h5>\n<p><span style=\"font-weight: 400;\">Using the example and information below, we can use this single rule to cover most of our important PII. Lets take this fake RDB table for example:<\/span><\/p>\n<table  class=\" table table-hover\" style=\"width: 546px;\">\n<tbody>\n<tr>\n<td style=\"width: 112px; text-align: center;\"><strong>ID Num (PK)<\/strong><\/td>\n<td style=\"width: 114px; text-align: center;\"><strong>First Name<\/strong><\/td>\n<td style=\"width: 134px; text-align: center;\"><strong>Last Name<\/strong><\/td>\n<td style=\"width: 85px; text-align: center;\"><strong>SSN<\/strong><\/td>\n<td style=\"width: 18px; text-align: center;\"><strong>State<\/strong><\/td>\n<td style=\"width: 83px; text-align: center;\"><strong>City<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 112px;\"><span style=\"font-weight: 400;\">1<\/span><\/td>\n<td style=\"width: 114px;\"><span style=\"font-weight: 400;\">Tom<\/span><\/td>\n<td style=\"width: 134px;\"><span style=\"font-weight: 400;\">Baker<\/span><\/td>\n<td style=\"width: 85px;\"><span style=\"font-weight: 400;\">111-11-1111<\/span><\/td>\n<td style=\"width: 18px;\"><span style=\"font-weight: 400;\">Texas<\/span><\/td>\n<td style=\"width: 83px;\"><span style=\"font-weight: 400;\">New York<\/span><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 112px;\"><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td style=\"width: 114px;\"><span style=\"font-weight: 400;\">Mike<\/span><\/td>\n<td style=\"width: 134px;\"><\/td>\n<td style=\"width: 85px;\"><\/td>\n<td style=\"width: 18px;\"><\/td>\n<td style=\"width: 83px;\"><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 112px;\"><span style=\"font-weight: 400;\">3<\/span><\/td>\n<td style=\"width: 114px;\"><\/td>\n<td style=\"width: 134px;\"><\/td>\n<td style=\"width: 85px;\"><\/td>\n<td style=\"width: 18px;\"><\/td>\n<td style=\"width: 83px;\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">In the table above, I am giving a few examples of where this rule would be useful. We can use any of these values to use as our deterministic lookup value, meaning, if I want to use the values from FirstName to mask\/generate data, we can do that.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Alternatively, in this situation, a better use would be to use the ID Num column, which contains values for all users, even if the other columns are not populated. For the records, I will be using the same rule, just asking for different return values.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Since I do not want to mask column 1, and it is a Primary Key (PK) \u2013 meaning, I will always get a unique value \u2013 I will use this column as my input data.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Inside the job specifications \u2013 either FieldShield (CoSort <a href=\"https:\/\/www.iri.com\/products\/cosort\/sortcl\">SortCL<\/a>) \/FIELD statements or DarkShield configuration (.dsc) files \u2013 the rules would appear as follows:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For column 2 (First Name):<\/span><\/p>\n<pre><span style=\"font-weight: 400;\">rule_name(\u201cfname\u201d, ${fieldname}, \u201cseedValue\u201d), where ${fieldname} == 1<\/span><\/pre>\n<p><span style=\"font-weight: 400;\">(for this specific column + record).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For column 3 (Last Name):<\/span><\/p>\n<pre><span style=\"font-weight: 400;\">rule_name(\u201clname\u201d, ${fieldname}, \u201cseedValue\u201d), where ${fieldname} == 1<\/span><\/pre>\n<p><span style=\"font-weight: 400;\">(for this specific column + record).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">And so forth, for record 1. For records 2 and 3 the function will do the same thing, except passing ${fieldname} as 2 and ${fieldname} as 3, respectively.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Below would be the output in a database table, for example:<\/span><\/p>\n<table  class=\" table table-hover\" >\n<tbody>\n<tr>\n<td><strong>ID Num (PK)<\/strong><\/td>\n<td><strong>First Name<\/strong><\/td>\n<td><strong>Last Name<\/strong><\/td>\n<td><strong>SSN<\/strong><\/td>\n<td><strong>State<\/strong><\/td>\n<td><strong>City<\/strong><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Brian<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Lane<\/span><\/td>\n<td><span style=\"font-weight: 400;\">829-01-8932<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Illinois<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Houston<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Serena<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Points<\/span><\/td>\n<td><span style=\"font-weight: 400;\">374-57-8257<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Pennsylvania<\/span><\/td>\n<td><span style=\"font-weight: 400;\">San Diego<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">3<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Mary<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Moore<\/span><\/td>\n<td><span style=\"font-weight: 400;\">192-83-7420<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Georgia<\/span><\/td>\n<td><span style=\"font-weight: 400;\">San Jose<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">As you can see, even when source values were not present, additional test data gets created, based simply on the PK provided in the database.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The same concepts apply to any source, including files, NoSQL DBs, etc. If a file contained free-floating text that contained \u201cThe patient in question was Robert Milnn.\u201d We can use this rule to replace both the First Name and the Last Name by just asking for a different return type. So our new masked text would become \u201cThe patient in question was Benny Suiin.\u201d<\/span><\/p>\n<h5><span style=\"font-weight: 400;\">How to use the Identity Fabrication Rule?<\/span><\/h5>\n<p><span style=\"font-weight: 400;\">This rule will be located inside the New Data Rule wizard in IRI Workbench, and called from the <\/span><a href=\"https:\/\/www.iri.com\/blog\/data-protection\/iri-data-classification\/\"><span style=\"font-weight: 400;\">Data Class and Rules Library<\/span><\/a><span style=\"font-weight: 400;\"> (iriLibrary.dcrlib), where all other rules reside. The exact location is under <\/span><i><span style=\"font-weight: 400;\">Masking -&gt; Deterministic -&gt; Non-reversible -&gt; Identity Fabrication<\/span><\/i><span style=\"font-weight: 400;\">:<\/span><\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18513\" src=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/identity-fabrication-wizard-245x300.png\" alt=\"\" width=\"460\" height=\"563\" srcset=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/identity-fabrication-wizard-245x300.png 245w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/identity-fabrication-wizard.png 510w\" sizes=\"(max-width: 460px) 100vw, 460px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">After selecting the \u201cIdentity Fabrication\u201d rule, a new window will appear:<\/span><\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18514\" src=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/pseudo-replacement-selection-300x184.png\" alt=\"\" width=\"498\" height=\"305\" srcset=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/pseudo-replacement-selection-300x184.png 300w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/pseudo-replacement-selection.png 506w\" sizes=\"(max-width: 498px) 100vw, 498px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">In this dialog, you would add a field name if you know the name of the field that you want to reference. If you do not know, leave it as i,s and the current field&#8217;s value will be used instead. You can also specify any ASCII value to act as a seed value.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The seed value maintains consistency, or determinism, between the data that is passed and the expected output. For example, if I pass in the word Tom with no seed value, I might get Carl. The next time, Tom might be replaced with Trisha instead.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If I specify the same seed each time, however, Tom would be Chris (for example) each time I run the job. Deterministic values that work from job to job support <\/span><a href=\"https:\/\/www.iri.com\/company\/faqs\/157\"><span style=\"font-weight: 400;\">referential integrity<\/span><\/a><span style=\"font-weight: 400;\"> in, and \u201cgolden copies\u201d of, test data targets.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For the selected type option, this will be the TYPE of value that is given back. In the image below, for example, the rule will return a value that looks like a formatted Social Security Number (xxx-xx-xxxx).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All the options are shown here, and selecting one of these options allows for a different return type:<\/span><\/p>\n<p style=\"text-align: center;\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-18515\" src=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/formatted-ssn-300x223.png\" alt=\"\" width=\"513\" height=\"382\" srcset=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/formatted-ssn-300x223.png 300w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/formatted-ssn.png 509w\" sizes=\"(max-width: 513px) 100vw, 513px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">After masking your selection and clicking \u201cFinish\u201d, the rule is now ready to be used in a FieldShield or DarkShield job.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you would like a walkthrough on how to run any of these jobs, please see the links below:<\/span><\/p>\n<p><a href=\"https:\/\/www.iri.com\/services\/training\/courseware#iri-fieldshield\"><span style=\"font-weight: 400;\">Running FieldShield Jobs<\/span><\/a><\/p>\n<p><a href=\"https:\/\/www.iri.com\/services\/training\/courseware#iri-darkshield\"><span style=\"font-weight: 400;\">Running DarkShield Jobs<\/span><\/a><\/p>\n<p><a href=\"https:\/\/www.iri.com\/blog\/data-protection\/iri-data-classification\/\"><span style=\"font-weight: 400;\">Configuring the Data Class &amp; Rule Library<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is the Identity Fabrication Rule? The identity fabrication rule is a deterministic but non-reversible data masking rule that can help you create realistic test PII for a whole record from just one original value. The new function, configurable from IRI Workbench, creates meaningful and unique new values by pseudonymizing an initial value (e.g., a<\/p>\n<div><a class=\"btn-filled btn\" href=\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/\" title=\"Fabricating PII\">Read More<\/a><\/div>\n","protected":false},"author":204,"featured_media":18518,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[8,29],"tags":[20,340,14,2416,18,2172,15,1813,2415,2179,2171,1388,520,850,2414,1815,22,1639,191],"class_list":["post-18509","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-protection","category-test-data","tag-data-anonymization","tag-data-governance","tag-data-masking","tag-data-mocking","tag-data-privacy","tag-data-rule-configuration","tag-data-security","tag-deterministic-masking","tag-fake-data","tag-fake-pii","tag-identity-fabrication","tag-iri-darkshield","tag-iri-fieldshield","tag-iri-workbench","tag-mock-pii","tag-pii-protection","tag-pseudonymization","tag-synthetic-data","tag-test-data-generation"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.4 (Yoast SEO v23.4) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Fabricating PII - IRI<\/title>\n<meta name=\"description\" content=\"Explore the identity fabrication rule for creating realistic PII. Learn how to pseudonymize data effectively and efficiently.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fabricating PII\" \/>\n<meta property=\"og:description\" content=\"Explore the identity fabrication rule for creating realistic PII. Learn how to pseudonymize data effectively and efficiently.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/\" \/>\n<meta property=\"og:site_name\" content=\"IRI\" \/>\n<meta property=\"article:published_time\" content=\"2025-07-30T23:54:22+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-04T22:02:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1152\" \/>\n\t<meta property=\"og:image:height\" content=\"552\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Tylor Quinley\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@tylorq@iri.com\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Tylor Quinley\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/\"},\"author\":{\"name\":\"Tylor Quinley\",\"@id\":\"https:\/\/www.iri.com\/blog\/#\/schema\/person\/287497b6de7a30d7c674b81c7cbdbbb1\"},\"headline\":\"Fabricating PII\",\"datePublished\":\"2025-07-30T23:54:22+00:00\",\"dateModified\":\"2026-03-04T22:02:13+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/\"},\"wordCount\":1000,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.iri.com\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png\",\"keywords\":[\"data anonymization\",\"data governance\",\"data masking\",\"data mocking\",\"data privacy\",\"Data Rule Configuration\",\"data security\",\"Deterministic Masking\",\"fake data\",\"fake PII\",\"Identity Fabrication\",\"IRI DarkShield\",\"IRI FieldShield\",\"IRI Workbench\",\"mock PII\",\"PII protection\",\"pseudonymization\",\"synthetic data\",\"test data generation\"],\"articleSection\":[\"Data Masking\/Protection\",\"Test Data\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/\",\"url\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/\",\"name\":\"Fabricating PII - IRI\",\"isPartOf\":{\"@id\":\"https:\/\/www.iri.com\/blog\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png\",\"datePublished\":\"2025-07-30T23:54:22+00:00\",\"dateModified\":\"2026-03-04T22:02:13+00:00\",\"description\":\"Explore the identity fabrication rule for creating realistic PII. Learn how to pseudonymize data effectively and efficiently.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#primaryimage\",\"url\":\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png\",\"contentUrl\":\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png\",\"width\":1152,\"height\":552},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.iri.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Fabricating PII\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.iri.com\/blog\/#website\",\"url\":\"https:\/\/www.iri.com\/blog\/\",\"name\":\"IRI\",\"description\":\"Total Data Management Blog\",\"publisher\":{\"@id\":\"https:\/\/www.iri.com\/blog\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.iri.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.iri.com\/blog\/#organization\",\"name\":\"IRI\",\"url\":\"https:\/\/www.iri.com\/blog\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.iri.com\/blog\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/02\/iri-logo-total-data-management-small-1.png\",\"contentUrl\":\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/02\/iri-logo-total-data-management-small-1.png\",\"width\":750,\"height\":206,\"caption\":\"IRI\"},\"image\":{\"@id\":\"https:\/\/www.iri.com\/blog\/#\/schema\/logo\/image\/\"}},{\"@type\":\"Person\",\"@id\":\"https:\/\/www.iri.com\/blog\/#\/schema\/person\/287497b6de7a30d7c674b81c7cbdbbb1\",\"name\":\"Tylor Quinley\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.iri.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/103619c915af89371c31a18efe3ece45?s=96&d=blank&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/103619c915af89371c31a18efe3ece45?s=96&d=blank&r=g\",\"caption\":\"Tylor Quinley\"},\"sameAs\":[\"https:\/\/www.iri.com\/blog\/wp-admin\",\"https:\/\/x.com\/tylorq@iri.com\"],\"url\":\"https:\/\/www.iri.com\/blog\/author\/tylorq\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Fabricating PII - IRI","description":"Explore the identity fabrication rule for creating realistic PII. Learn how to pseudonymize data effectively and efficiently.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/","og_locale":"en_US","og_type":"article","og_title":"Fabricating PII","og_description":"Explore the identity fabrication rule for creating realistic PII. Learn how to pseudonymize data effectively and efficiently.","og_url":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/","og_site_name":"IRI","article_published_time":"2025-07-30T23:54:22+00:00","article_modified_time":"2026-03-04T22:02:13+00:00","og_image":[{"width":1152,"height":552,"url":"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png","type":"image\/png"}],"author":"Tylor Quinley","twitter_card":"summary_large_image","twitter_creator":"@tylorq@iri.com","twitter_misc":{"Written by":"Tylor Quinley","Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#article","isPartOf":{"@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/"},"author":{"name":"Tylor Quinley","@id":"https:\/\/www.iri.com\/blog\/#\/schema\/person\/287497b6de7a30d7c674b81c7cbdbbb1"},"headline":"Fabricating PII","datePublished":"2025-07-30T23:54:22+00:00","dateModified":"2026-03-04T22:02:13+00:00","mainEntityOfPage":{"@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/"},"wordCount":1000,"commentCount":0,"publisher":{"@id":"https:\/\/www.iri.com\/blog\/#organization"},"image":{"@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#primaryimage"},"thumbnailUrl":"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png","keywords":["data anonymization","data governance","data masking","data mocking","data privacy","Data Rule Configuration","data security","Deterministic Masking","fake data","fake PII","Identity Fabrication","IRI DarkShield","IRI FieldShield","IRI Workbench","mock PII","PII protection","pseudonymization","synthetic data","test data generation"],"articleSection":["Data Masking\/Protection","Test Data"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/","url":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/","name":"Fabricating PII - IRI","isPartOf":{"@id":"https:\/\/www.iri.com\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#primaryimage"},"image":{"@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#primaryimage"},"thumbnailUrl":"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png","datePublished":"2025-07-30T23:54:22+00:00","dateModified":"2026-03-04T22:02:13+00:00","description":"Explore the identity fabrication rule for creating realistic PII. Learn how to pseudonymize data effectively and efficiently.","breadcrumb":{"@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#primaryimage","url":"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png","contentUrl":"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png","width":1152,"height":552},{"@type":"BreadcrumbList","@id":"https:\/\/www.iri.com\/blog\/data-protection\/fabricating-pii\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.iri.com\/blog\/"},{"@type":"ListItem","position":2,"name":"Fabricating PII"}]},{"@type":"WebSite","@id":"https:\/\/www.iri.com\/blog\/#website","url":"https:\/\/www.iri.com\/blog\/","name":"IRI","description":"Total Data Management Blog","publisher":{"@id":"https:\/\/www.iri.com\/blog\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.iri.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.iri.com\/blog\/#organization","name":"IRI","url":"https:\/\/www.iri.com\/blog\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.iri.com\/blog\/#\/schema\/logo\/image\/","url":"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/02\/iri-logo-total-data-management-small-1.png","contentUrl":"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/02\/iri-logo-total-data-management-small-1.png","width":750,"height":206,"caption":"IRI"},"image":{"@id":"https:\/\/www.iri.com\/blog\/#\/schema\/logo\/image\/"}},{"@type":"Person","@id":"https:\/\/www.iri.com\/blog\/#\/schema\/person\/287497b6de7a30d7c674b81c7cbdbbb1","name":"Tylor Quinley","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.iri.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/103619c915af89371c31a18efe3ece45?s=96&d=blank&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/103619c915af89371c31a18efe3ece45?s=96&d=blank&r=g","caption":"Tylor Quinley"},"sameAs":["https:\/\/www.iri.com\/blog\/wp-admin","https:\/\/x.com\/tylorq@iri.com"],"url":"https:\/\/www.iri.com\/blog\/author\/tylorq\/"}]}},"jetpack_featured_media_url":"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2025\/07\/featured-image-fabricating-pii.png","_links":{"self":[{"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/posts\/18509"}],"collection":[{"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/users\/204"}],"replies":[{"embeddable":true,"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/comments?post=18509"}],"version-history":[{"count":7,"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/posts\/18509\/revisions"}],"predecessor-version":[{"id":19056,"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/posts\/18509\/revisions\/19056"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/media\/18518"}],"wp:attachment":[{"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/media?parent=18509"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/categories?post=18509"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.iri.com\/blog\/wp-json\/wp\/v2\/tags?post=18509"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}