FieldShield vs. DarkShield for RDB Masking
The IRI Voracity Data Management platform and the IRI Data Protector suite provide robust data masking tools for finding and anonymizing sensitive data in different environments. When working with relational database (RDB) sources, however, its two key products come into play: FieldShield and DarkShield. Both tools help secure personally identifiable information (PII) and other sensitive data in database environments. However, they differ in their functional scope, discovery capabilities, and integration methods.
Functional Comparison Matrix
The following overview highlights the key differences between FieldShield and DarkShield.
| Feature/Aspect | FieldShield | DarkShield |
| Primary Focus | Structured data masking in relational databases | Discovery and masking across structured and semi/unstructured RDB columns |
| Data Types Supported | Strictly structured fields (tables, columns) | Structured fields plus semi/unstructured text within RDB columns |
| Discovery Capabilities | Data classification (search) is performed before masking, though it uses the same data classes and masking rules as DarkShield. Limited to metadata (location-based) matchers for discovery. | Simultaneous search and masking of sensitive data across schema using the same data classes and masking functions as FieldShield. Uses location and/or data (content) matchers as needed. |
| Masking Methods | Deterministic, encryption, pseudonymization, redaction, etc. | Same masking methods, extended to free-text and mixed data formats |
| Integration | Deep integration with Voracity ETL via the use of CoSort SortCL syntax | Supported in Voracity, can create SortCL DDF for textual ETL use |
| Ease of Use | Requires schema knowledge and manual setup | Wizard-driven GUI in IRI Workbench for RDB masking jobs (search + mask simultaneously) |
| Audit & Logging | Produces an audit trail of search results and masking job details; integrates with CoSort V11 OGS logging | Enhanced audit logs for discovery and masking jobs across mixed RDB fields |
| Best Fit | Traditional relational database environments with well-defined schemas | Hybrid environments where sensitive data may reside in both structured and semi/unstructured RDB fields |
Use Case Matrix
Different environments may favor one tool over the other depending on how the data is stored.
| Use Case Type | Best Tool | Example Scenario |
| Classic RDB Masking | FieldShield | Masking SSNs in a customer table with a defined schema |
| Hybrid RDB Masking | DarkShield | Searching and masking PII in free-text notes stored in VARCHAR columns |
| Compliance (GDPR, HIPAA) | Both | Ensuring sensitive fields are anonymized before reporting |
| Data Discovery | DarkShield | Automatically finding sensitive data in unexpected RDB columns |
| ETL Integration | FieldShield | Applying masking during ETL workflows for structured warehouse loads |
| Audit & Governance | Both | FieldShield produces detailed audit trails (including CoSort V11 OGS logs); DarkShield provides extended discovery and masking logs as well |
| Operational Oversight | Both | Tracking search results, masking jobs, data recovery, and compliance reporting |
Decision Flowchart
Here’s a simple guide for choosing between FieldShield and DarkShield in RDB contexts:
Conclusion
- FieldShield is ideal for structured RDB environments where sensitive fields are known, and masking can be applied before (or during Voracity) ETL or BI/AI workflows.
- DarkShield is best for hybrid RDB environments containing semi/unstructured text, or when simultaneous discovery of sensitive data is required for high-volume efficiency.
- Both tools support data privacy, compliance, and governance, with FieldShield offering enhanced integration into CoSort V11’s OGS logging environment for detailed audit trails.
Both FieldShield and DarkShield are inside the IRI Voracity data management platform, which also supports RowGen, CoSort, NextForm, Ripcurrent, FACT, and more. Please email info@iri.com if you have any questions or would like more information.
