Data-Centric Security

Data Masking

Our Data Masking Software in Gartner Market Guide 2020

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Find and Fix Data at Risk

Classify PII centrally, find it globally, and mask it automatically. Preserve realism and referential integrity using encryption, pseudonymization, redaction, and other rules for production and test envrionments.
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Comply with Data Privacy Laws
Delete, deliver, or anonymize data subject to CIPSEA, DPA, FERPA, GDPR, GLBA, HIPAA, PCI, POPI, etc. Verify compliance via human- and machine-readable search reports, job audit logs, and re-identification risk scores.
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Protect Data throughout its Lifecycle
Optionally mask data as you map it. Apply FieldShield functions in IRI Voracity ETL, federation, migration, replication, subsetting, or analytic jobs. Or, run FieldShield from Actifio or Commvault jobs to mask DB clones.
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FieldShield Use Cases


  • Payment Card Industry Data (PCI)

    "FieldShield decrypts and re-encrypts fields in our credit card migration and test sources, and easily generates and manages encryption keys."

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  • Protected Health Information (PHI)

    "We continue to rely on FieldShield for flat-file and DB de-identification in order to comply with government healthcare privacy regulations."

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  • Personally Identifiable Information (PII)

    "We use FieldShield to anonymize HR data in complex file feeds, to segment and substitute values based on field-level conditions."

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Do You Need FieldShield?

Take the FieldShield Quiz

  • Do you collect or process PII or other "data at risk"? Do you know where (all of) it is?
  • Is that data safe from a breach; i.e., could it be used were it stolen or exposed?
  • Does your department comply with data privacy regulations? Can you prove it?
  • Do you use multiple tools or methods to protect different DB columns in different ways?
  • Can you protect only the data at risk, so you can see and use the non-sensitive data?
  • Does your masked data look real enough? Is it referentially correct?
  • Can you score your masked data sets for re-ID risk and anonymize quasi-identifers?
  • Does it take too long to learn, implement, modify, or optimize your data masking jobs?
  • Can you mask data in your ETL, subsetting, migration/replication, CDC or reporting tasks?

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Complete Data Masking

Every Source:

Flat Files


Semi-structured Files

Mainframe & Index Files

S3, HDFS, MQTT & Kafka 

Pipes, Procedures & URLs

Excel Spreadsheets  (CellShield)

Unstructured Files & Faces (DarkShield)

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Every Protection:

Blur or Bucket

Bit Shift/Scramble

Encrypt & Decrypt

Encode & Decode

Delete or Redact

Hash or Tokenize

Pseudonymize & Restore

Random Generate or Select

Custom (New Field) Functions

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 Every Deployment:

Eclipse IDE

Command Line

Batch/Shell Scripts

Ad Hoc or Scheduled

In-situ/SQL Procedures

System/API Library Calls

Replication, Test & DevOps 

Incremental Update/Refresh

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IRI Defines Startpoint Security | Outlook Series

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Learn more about FieldShield

Data Masking White Paper

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What Others Are Reading

  • Data Masking vs. Data Encryption

    Do you know the differences between them? Learn about these two popular forms of data obfuscation and when to use them.

    Read now.

  • Which Masking Function is Best?

    Read this review of the important decision criteria, including realism, reversibility, consistency, speed, and security.

    Read now.

  • PCI Tokenization in FieldShield

    The Payment Card Industry Data Security Standard, or PCI DSS, requires encryption or tokenization of primary account numbers.

    Read now.