{"id":19046,"date":"2026-03-06T17:30:57","date_gmt":"2026-03-06T22:30:57","guid":{"rendered":"https:\/\/www.iri.com\/blog\/?p=19046"},"modified":"2026-03-27T19:48:39","modified_gmt":"2026-03-27T23:48:39","slug":"fieldshield-vs-darkshield-for-rdb-masking","status":"publish","type":"post","link":"https:\/\/www.iri.com\/blog\/data-protection\/fieldshield-vs-darkshield-for-rdb-masking\/","title":{"rendered":"FieldShield vs. DarkShield for RDB Masking"},"content":{"rendered":"

The IRI Voracity Data Management platform and the <\/span>IRI Data Protector<\/span><\/a> suite provide robust data masking tools for finding and anonymizing sensitive data in different environments. When working with relational database (RDB) sources, however, two key IRI products come into play: <\/span>FieldShield<\/span><\/a> and <\/span>DarkShield<\/span><\/a>. <\/span><\/p>\n

Both tools are priced the same for RDB use, and share the same front-end IRI Workbench GUI<\/a>. Both also use the same data classes<\/a> and masking functions<\/a> to secure personally identifiable information (PII) and other sensitive data in database environments. However, they differ somewhat in functional scope, discovery capabilities, runtime engines, and job configurations.<\/span><\/p>\n

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Functional Comparison Matrix<\/b><\/h5>\n

The following overview highlights the key differences between FieldShield and DarkShield.<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
Feature\/Aspect<\/b><\/span><\/td>\nFieldShield<\/b><\/span><\/td>\nDarkShield<\/b><\/span><\/td>\n<\/tr>\n
Primary Focus<\/b><\/td>\nStructured data masking in relational databases<\/span><\/td>\nDiscovery and masking across structured <\/span>and<\/span><\/i> semi\/unstructured RDB columns<\/span><\/td>\n<\/tr>\n
Data Types Supported<\/b><\/td>\nStrictly structured fields (tables, columns)<\/span><\/td>\nStructured fields plus<\/em> semi\/unstructured text or files in RDB columns<\/span><\/td>\n<\/tr>\n
Discovery Capabilities<\/b><\/td>\nData classification<\/span><\/a> (search) is performed before<\/em> masking, though it uses the same data classes and masking rules as DarkShield. Limited to metadata (location-based) matchers for discovery.<\/span><\/td>\nSimultaneous<\/em> search and masking of sensitive data in a schema using the same data classes and <\/span>masking functions<\/span><\/a> as FieldShield. <\/span>Uses location and\/or data (content) matchers as needed.<\/span><\/td>\n<\/tr>\n
Masking Methods<\/b><\/td>\nDeterministic, encryption, pseudonymization, redaction, etc.<\/span><\/td>\nSame masking methods, extended to free-text and mixed data formats<\/span><\/td>\n<\/tr>\n
Integration<\/b><\/td>\nDeep integration with Voracity ETL via the use of the CoSort <\/span>SortCL<\/span><\/a> syntax and program for its masking jobs<\/span><\/td>\nSupported in Voracity, can create\u00a0 SortCL DDF for <\/span>textual ETL<\/span><\/a> use, but uses a REST API<\/a> for search\/mask<\/span><\/td>\n<\/tr>\n
Ease of Use<\/b><\/td>\nRequires schema knowledge and more setup steps<\/a><\/span><\/td>\nWizard-driven GUI<\/a> in IRI Workbench for RDB masking jobs (search + mask simultaneously)<\/span><\/td>\n<\/tr>\n
Audit & Logging<\/b><\/td>\nProduces an audit trail of search results and masking job details; integrates with CoSort V11<\/a> OGS logging<\/span><\/td>\nEnhanced audit logs for discovery and masking jobs across mixed RDB columns<\/span><\/td>\n<\/tr>\n
Best Fit<\/b><\/td>\nStructured relational database environments with well-defined schemas<\/span><\/td>\nHybrid environments where sensitive data may reside in both structured and semi\/unstructured RDB columns<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Use Case Matrix<\/b><\/h5>\n

Different environments may favor one tool over the other depending on how the data is stored.<\/p>\n\n\n\n\n\n\n\n\n\n\n
Use Case Type<\/b><\/span><\/td>\nBest Tool<\/b><\/span><\/td>\nExample Scenario<\/b><\/span><\/td>\n<\/tr>\n
Classic RDB Masking<\/b><\/td>\nFieldShield<\/span><\/td>\nMasking SSNs in a customer table with a defined schema with advanced business logic<\/span><\/td>\n<\/tr>\n
Hybrid RDB Masking<\/b><\/td>\nDarkShield<\/span><\/td>\nSearching and masking PII in free-text notes stored in VARCHAR columns amid complex constraints<\/span><\/td>\n<\/tr>\n
Compliance (GDPR, HIPAA)<\/b><\/td>\nBoth<\/span><\/td>\nEnsuring sensitive fields are anonymized before reporting and DevOps<\/span><\/td>\n<\/tr>\n
Data Discovery<\/b><\/td>\nDarkShield<\/span><\/td>\nAutomatically finding sensitive data in unexpected RDB columns during masking<\/span><\/td>\n<\/tr>\n
ETL Integration<\/b><\/td>\nFieldShield<\/span><\/td>\nApplying masking during ETL workflows for structured, anonymized data warehouse loads<\/span><\/td>\n<\/tr>\n
Audit & Governance<\/b><\/td>\nBoth<\/span><\/td>\nFieldShield produces detailed audit trails (including CoSort V11 OGS logs); DarkShield provides extended discovery and masking logs as well<\/span><\/td>\n<\/tr>\n
Operational Oversight<\/b><\/td>\nBoth<\/span><\/td>\nTracking search results, masking jobs, data recovery, and compliance reporting<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Decision Flowchart<\/b><\/h5>\n

Here\u2019s a simple guide for choosing between FieldShield and DarkShield in RDB contexts:<\/span><\/p>\n

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Conclusion<\/b><\/h5>\n