{"id":16523,"date":"2023-12-19T12:37:15","date_gmt":"2023-12-19T17:37:15","guid":{"rendered":"https:\/\/www.iri.com\/blog\/?p=16523"},"modified":"2024-01-17T08:48:31","modified_gmt":"2024-01-17T13:48:31","slug":"iri-data-classification","status":"publish","type":"post","link":"https:\/\/beta.iri.com\/blog\/data-protection\/iri-data-classification\/","title":{"rendered":"Data Class & Rule Library in IRI Workbench"},"content":{"rendered":"

Abstract:<\/b> HIPAA, GDPR, FERPA, and other data privacy laws require that personally identifiable information (PII) and related data considered sensitive be protected from disclosure or discovery. A list of sensitive items made up of PII can be abstracted into easy-to-understand groups – what IRI calls Data Classes – to produce groupings of PII such as emails, names, phone numbers, etc. <\/span><\/em><\/p>\n

In this article, Data Classes and the Data Class & Rule Library are discussed in depth. It covers how Data Classes and (data masking) Rules are defined and stored inside the IRI Workbench<\/a> Data Class & Rules Library. Once inside, that information can be accessed by IRI Voracity job-building wizards for NextForm, FACT , RowGen, FieldShield, and DarkShield — as well as the FieldShield Schema Data Class Search or Directory Data Class Search wizards.\u00a0<\/span><\/em><\/p>\n

Note that a Data Class & Rules Library is different from a Data Class Map<\/a>, which would be the final product of a Schema Data Class Search<\/a> or Directory Data Class Search<\/a> wizard. Those wizards use a Data Class & Rules Library to perform data classification and produce a Data Class Map containing mappings of rules to fields in structured targets.<\/span><\/em><\/p>\n

Editors Note:<\/strong> The information in this article supersedes earlier<\/a> documentation on data class creation in IRI Workbench versions downloaded prior to December 2023 and that an upcoming article covers auto-migration of your data classes and masking rules to the new, improved framework described herein.<\/p>\n

 <\/p>\n

What is Data Classification?<\/b>
\n<\/b><\/h4>\n

\"\"<\/p>\n

Depending on who you ask, the meaning of data classification may differ. At IRI, data classification refers to the act of cataloging and defining specific types of data \u2013 like email addresses, ID numbers, and company names \u2013 into unique, abstract categories of data called Data Classes, based on certain attributes or traits.\u00a0<\/span><\/p>\n

IRI Data Class and Rule Library<\/b><\/h4>\n

IRI Data Classes and IRI Rules are stored in a file called IRI Data Class and Rule Library.<\/span><\/p>\n

\"\"<\/p>\n

IRI Data Class (& Rules) Library Form Editor<\/b><\/p>\n

Every new IRI Project produced in IRI Workbench comes with an empty Data Class and Rule Library with a file extension of .<\/span>dcrlib<\/span><\/i>. This library is where Data Classes and Rules are created and stored.<\/span><\/p>\n

Every FieldShield Job and DarkShield Job requires at least one Data Class to be present in a project\u2019s IRI Data Class and Rules Library. To create, edit, and remove Data Classes and Rules, you must use the IRI Library Form Editor. The IRI Library Form Editor provides a non-modal graphical user interface that allows for the configuration of the IRI Data Class and Rules Library.<\/span><\/p>\n

To open the IRI Library Form Editor, double-click on the <\/span>iriLibrary.dcrlib<\/span><\/i> file inside your project folder. This in turn opens the respective form editor attached to the <\/span>iriLibrary.dcrlib<\/b> file inside Workbench as a non-modal wizard page.\u00a0<\/span><\/p>\n

In the form editor, you can add Data Class Groups, Data Classes and their associated Search Matchers and default Rule, and also Rules not associated with any of the Data Classes. These unassigned Rules can later be used to overwrite the default Rule assigned to each Data Class.<\/span><\/p>\n

Data Classes<\/b><\/h4>\n

Data Classes provide convenience, consistency, and the ability to support the needs of data architects and governance teams by providing a more granular level of control on what is considered, discovered, and treated as PII. Data Classes consist of Search Matchers, a default Rule, and RDB Column Type filters (only applicable to Relational Databases).<\/span><\/p>\n

By default when a new IRI Project is created a Data Class Rules Library comes preloaded with several Data Classes with some default Rules assigned. To add a new Data Class to the library, double click the green tag \u00a0\"\"\u00a0<\/span><\/span> at the top of the form editor to start creating a Data Class. After clicking the green tag, a page will appear to prompt the user with a unique name for the Data Class you are creating.\u00a0<\/span><\/p>\n

Note that there can not be multiple Data Classes with the same name. Click <\/span>Ok <\/span><\/i>and a new Data Class should populate the library.<\/span><\/p>\n

For example:<\/span><\/p>\n

\"\"<\/p>\n

\"\"Data Class Groups<\/b><\/h4>\n

A Data Class Group is a container for a group of data classes. Each Data Class Group can have a default Rule assigned by the user.\u00a0<\/span><\/p>\n

By assigning a default Rule to a Data Class Group, any Data Classes within a Data Class Group that have no default Rule assigned will instead inherit the default Rule of the parent Data Class Group. Otherwise, if the Data Class in a Group has a default Rule, that Rule will be used instead of the Data Class Group\u2019s default Rule. Grouping Data Classes together can also be useful for categorization and logging purposes.<\/span><\/p>\n

Another optional feature of Data Class Groups is the ability to further categorize Data Classes according to their level of sensitivity. Sensitivity level groups are Data Class Groups with an assigned priority level. Higher priority groups typically have more restrictive masking functions assigned to them.\u00a0<\/span><\/p>\n

Because only one Data Class can be matched to a given element of PII, the sensitivity level of a Data Class Group determines the order in which a Data Class that may be in a different group (using different masking rules) can perform its matching and masking operation against incoming data. Where two Data Classes with the same name and search matchers but different masking functions are defined, the sensitivity level should dictate which masking function takes priority.<\/span><\/p>\n

In the example below, the License_Plate_Number data class might be found in both Proprietary and Sensitive groups. Sensitive is the higher priority sensitivity level group, so in this case the redaction rule would be applied even though the License_Plate_Number was also part of the Proprietary group which had a default encryption rule assigned.<\/span><\/p>\n

\"\"<\/p>\n

To create a Data Class Group, double click on the multi green tag \"\"<\/span> icon. From the pop up screen, provide a unique name for the object and indicate if you wish to have Sensitivity Levels generated inside the Group object. Lastly, click OK to finalize and generate a new Data Class Group.<\/span><\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

Privacy Law Associated Data Class Groups<\/b><\/h4>\n

Privacy Law Groups are pre-populated Data Class Groups that provide a launching board for business rules to adhere to different privacy law requirements. These privacy law groups have pre-populated data classes, search matchers, and masking functions.\u00a0<\/span><\/p>\n

Note however that these specifications are provided for convenience, and may or may not identify every element or conform to specific data protection requirements. <\/span><\/p>\n

Review and customization of these objects is therefore recommended to assure your job settings will address your particular needs.<\/span><\/p>\n

To create a Privacy Law Group double click the courthouse \"\"\u00a0<\/span><\/span> icon at the top of the editor. From the pop up screen, indicate which privacy law template you wish to use for your new Privacy Law Group, then click OK.<\/span><\/p>\n

\"\"<\/p>\n

\"\"<\/p>\n

Search Matchers<\/b><\/h4>\n

Currently, Search Matchers can be divided into two sub-categories: <\/span>Location Matchers<\/b> and <\/span>Data Matchers<\/b>. Location Matchers apply strictly to structured and semi-structured data and inspect the structure of data. Data matchers on the other hand, directly inspect the contents of data to determine if they match the specified search attributes of the data class.<\/span><\/p>\n

As a general rule, Location Matchers have better performance during matching operations and have better accuracy. The caveat is that Location Matchers are not available when working with unstructured data, since Location Matchers rely on a predefined structure to match on PII.<\/span><\/p>\n

Unlike Location Matchers, Data Matchers can be used for matching against structured, semi-structured, and unstructured data. Data Matchers are very useful when PII can be found in free floating text. This includes but is not limited to, text files, Word documents, PDFs, images, and PowerPoint slides.\u00a0<\/span><\/p>\n

By using <\/span>both <\/span><\/i>location and data matchers simultaneously, you can find PII in your data source(s) by either source structure or data format. Note as well that you can also use more than one location matcher and data matcher at the same time for even more certainty, but the more matching attempts you specify, the longer data discovery can take. Without at least one Search Matcher, no matches will be found, and no grouping of data classes can occur.<\/span><\/p>\n

The IRI Library form editor provides a section called Matcher Details which allows for the adding, editing, and removal of Search Matchers from individual Data Classes. Currently, Search Matchers are divided into two sub-categories: Location Matchers and Data Matchers.\u00a0<\/span><\/p>\n

The Matcher Details section supports the creation of multiple Location and Data Matchers.\u00a0<\/span><\/p>\n

\"\"<\/p>\n

Below are links to articles that discuss Location Matchers and Data Matchers in more depth, including how each are specified:<\/span><\/p>\n

    \n
  1. Location Matchers<\/span><\/a><\/li>\n
  2. Data Matchers<\/span><\/a><\/li>\n<\/ol>\n

    Data Class Default Rule<\/b><\/h4>\n

    Any Data Class can store a default rule. A default rule is usually a masking function<\/a> that will be applied by default to a Data Class if no Rule has been assigned. This is done to assure there is at least a base level of protection assigned to a Data Class if this is a Data Class with sensitive PII.<\/span><\/p>\n

    To create a default rule, select the <\/span>Create\u2026<\/span><\/i> button to the right of the Default Rule label and a dialog will appear. There are three major types of rules: Data, Quality, and Section. Data rules is the most common type, and are used for data masking. At the top of the dialog, there is a filter section to expose masking rules which only relate to DarkShield.<\/span><\/p>\n

    \"\"<\/p>\n

    Once a rule is created, select the drop-down menu next to the Default Rule label and select a rule to be the default for that data class.<\/span><\/p>\n

    \"\"<\/p>\n

    Rules Library<\/b><\/h4>\n

    In addition to the default rule inside a Data Class, some rules can exist without being assigned to a Data Class during its creation. These unassigned rules are stored in a sub-library called the <\/span>Rules Library<\/span><\/i>.\u00a0<\/span><\/p>\n

    The rules in a Rules Library are available during the creation of an IRI job, and allow for the overriding of the default rule assigned to a data class if your application requires that. To access the Rules Library from the IRI Library Editor, click on the red toolbox next to the words \u201cRules\u201d.\u00a0<\/span><\/p>\n

    A view of current existing rules will be displayed in the editor. From the editor, we should also be able to add, modify, or remove rules as needed.<\/span><\/p>\n

    \"\"<\/p>\n

    Rule Pro Edit<\/b><\/h4>\n

    \"\"<\/p>\n

    The Pro Edit<\/span> option in the Rule Library editor allows for manual modification of Rules without the need to go through a wizard for that. This option helps those with advanced knowledge and the need for more freedom in alteration, like adding additional properties to a rule.\u00a0<\/span><\/p>\n

    One example use might be changing the data type in a target column (within a FieldShield job script). Clicking <\/span>Pro Edit<\/span><\/i> opens a page for editing a rule\u2019s properties.\u00a0<\/span><\/p>\n

    \"\"<\/p>\n

    From this page, Rule properties can be added, modified, or removed. <\/span>Pro Editor also allows you to change or add properties that you couldn’t through the standard creation of rules.\u00a0<\/span><\/p>\n

    Example 1) I can add a property that changes the data type of a field when we generate <\/span>SortCL <\/span><\/a>job (e.g., FieldShield data masking) scripts.\u00a0<\/span><\/p>\n

    Example 2) In the image I can edit the expression to use an<\/span> if then else<\/span><\/i> statement and it will generate it in job scripts automatically.<\/span>\u00a0<\/span><\/p>\n

    Using this wizard requires knowledge of IRI data rules and their possible properties. That said, all IRI rule properties and expressions are documented SortCL program options in the CoSort manual.\u00a0<\/span><\/p>\n

    \"\"<\/p>\n

    Import From Old Data Class Library and Rules Library<\/b><\/h4>\n

    \"\"<\/p>\n

    Before the Data Class and Rule Library (<\/span>.dcrlib<\/span><\/i>) was supported in IRI Workbench there were predecessor libraries called the Data Class Library and the Rules Library. With breaking changes to updates to IRI Workbench and new features added, the old libraries became unusable. As such users needed a way to make the transition from older versions to new ones without having to completely throw out their previous work.\u00a0<\/span><\/p>\n

    The Data Class and Rules Library now supports the import and conversion of content from the older versions of the Data Class Library and Rules Library:<\/span><\/p>\n

    \"\"<\/p>\n

    Documentation of this functionality <\/span>is available in <\/span>this<\/span><\/a> article.<\/span><\/p>\n

     <\/p>\n","protected":false},"excerpt":{"rendered":"

    Abstract: HIPAA, GDPR, FERPA, and other data privacy laws require that personally identifiable information (PII) and related data considered sensitive be protected from disclosure or discovery. A list of sensitive items made up of PII can be abstracted into easy-to-understand groups – what IRI calls Data Classes – to produce groupings of PII such as<\/p>\n

    Read More<\/a><\/div>\n","protected":false},"author":152,"featured_media":16529,"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":[8,217,91,29],"tags":[1304,1081],"class_list":["post-16523","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-protection","category-iri","category-iri-workbench","category-test-data","tag-data-class","tag-data-classification"],"acf":[],"yoast_head":"\nData Class & Rule Library in IRI Workbench - IRI<\/title>\n<meta name=\"description\" content=\"Abstract: HIPAA, GDPR, FERPA, and other data privacy laws specify what personally identifiable information (PII) or other sensitive data is considered sensitive enough to require protection from disclosure or discovery. This list of sensitive items \u2013 what IRI calls data classes \u2013 can be abstracted into easy-to-understand groups such as emails, names, phone numbers, etc. This article describes how these Data Classes \u2013 and the ways to find and mask them \u2013 are defined in IRI Workbench. This information is then available to any IRI job that selects that IRI Data Class Rules Library.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/beta.iri.com\/blog\/data-protection\/iri-data-classification\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Class & Rule Library in IRI Workbench - IRI\" \/>\n<meta property=\"og:description\" content=\"Abstract: HIPAA, GDPR, FERPA, and other data privacy laws specify what personally identifiable information (PII) or other sensitive data is considered sensitive enough to require protection from disclosure or discovery. This list of sensitive items \u2013 what IRI calls data classes \u2013 can be abstracted into easy-to-understand groups such as emails, names, phone numbers, etc. This article describes how these Data Classes \u2013 and the ways to find and mask them \u2013 are defined in IRI Workbench. 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