Don't be the next victim of a data breach. Comply with data privacy laws. Protect personally identifiable information (PII), prevent data loss, and promote data governance goals.
Got Private Data?
FieldShield shields fields with the compliance industry's broadest array of protection functions. FieldShield provides role-based access controls for sensitive data on a static or dynamic basis to help you comply with CIPSEA, GLBA, HIPAA/HITECH, PCI DSS, SOX, and international data privacy laws like the DPA, EU Data Privacy Directive, PDPA, PIPEDA, and POPI.
FieldShield is the best way to:
- Profile, protect, present, and populate data in a familiar integrated development environment (IDE)
- Mask big data using your existing systems -- without Hadoop, in-memory databases, or appliances
- De-identify data in one or more sources simultaneously based on your business rules
- Specify protection conditions and rules across tables for design efficiency and referential integrity
- Encrypt and remove the individualizing characteristics of data without cutting off access to non-sensitive data, or changing its basic appearance
- Auto-create XML audit logs to help verify compliance with data privacy laws
- Fold data masking into data transformation, migration, replication, loading, federation, and BI activities
Take the FieldShield Quiz
Personally identifiable information (PII), protected health information (PHI), credit card / primary account number (PAN), and other confidential data are commonly stored in databases (DB), flat files, and applications like Excel. Names, addresses, account and contact numbers, etc., are in motion through DB and other applications, and at rest on networks and devices. Whether in transit or at rest, proper stewardship of this data is required to avoid lawsuits, privacy law violations, and reputational damage.
Consider these questions:
- Do you know you have data at risk? Do you know where (all of) it is?
- Are you safe from data breaches? Would your data still be safe if it were exposed or taken?
- Does your department comply with data privacy regulations? Can you prove it?
- Are you using multiple tools or methods to protect different sources in different ways?
- Do you protect only the data at risk, so you can see and use non-sensitive data?
- Does the protected data look real enough? Is it referentially correct?
- Can you protect data in real-time or in database applications dynamically?
- Can you seamlessly combine data masking with data transformation, migration, and reporting?
- How long does it take for you to learn, implement, modify, or optimize your data masking jobs?
- Is your source and security function metadata simple, reusable, shareable, and interoperable?