Health Care

The move towards EHRs is truly a global phenomenon.

As part of this process, new interoperable systems need to be created to facilitate data storage, transmission, usage, and sharing. Testing these systems with real data compromises patient privacy and risks theft from insiders; yet, realistic data is required to ensure these systems function properly. Using Camouflage to create realistic data for use in these non-production environments is the solution.

With HIPAA enforcement on the rise, and new penalties introduced through the HITECH Act, protecting electronic PHI in non-production environments is becoming increasingly more important. Compliance with Section 306 of the HIPAA Security Rule requires covered entities to “protect against any reasonably anticipated threats or hazards to the security or integrity [of electronic PHI].” As such, they are obliged to protect against data theft by developers and testers during application/system development. The most “reasonable and appropriate” way to protect against this threat is to use data masking to create realistic data for use in these non-production environments. Effectively, Camouflage masking will produce de-identified data which also ensures compliance with the HIPAA Privacy Rule.

Violations of patient privacy by health care organizations have a devastating impact on patient trust, and in turn their willingness to do business – no CEO, CIO, or CISO wants to have a breach of PHI on their watch, and no information security manager or DBA wants to become a scapegoat. Health care data breaches make great news stories which usually require terminations and media statements that waste time and energy, and often have no noticeable impact in restoring patient trust. Expensive lawsuits are inevitable.

Using Camouflage to create realistic data will protect against data theft.

• Create and test EHRs, diagnostic applications, and health information exchanges.
• Test system interoperability.
• Facilitate data sharing by using Camouflage to de-identify data, create limited data sets, or reduce data to the “minimum necessary” for the intended purpose.