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Automated Synthetic Test Data Generation & PII Masking for Healthcare
A HealthTech platform processing electronic health records (EHR) and insurance claims under strict HIPAA privacy compliance.
Runs onPariksha- HIPAA-compliant testing environment with zero real patient data
- 100%HIPAA-compliant testing environment with zero real patient data
- test data preparation time cut
- 2 weekstest data preparation time cut
How the work runs
The pressure that made this worth automating, the steps the system runs, and what came out the other side.
Pressure & Trigger Points
- Testing complex claims workflows required realistic patient data sets with diverse medical histories and insurance rules.
- Using production data copies in test environments violated HIPAA regulations and created severe data breach risks.
- Manual creation of complex synthetic test records took weeks and lacked boundary condition variety.
The run · 5 operational steps
Click any step to inspect telemetry signals, model reasoning, and governance gates.
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Schema & Dependency Learning
Pariksha analyzes database schemas and relational constraints without reading raw production PII data.
Input Signal:
Real-time operational telemetry & queue
Reasoning Pattern:
MCP grounded vector inference
Governance Gate:
Policy constrained with audit write-back
Verified Business Outcomes
- 100% HIPAA-compliant testing environment with zero real patient data exposure.
- Test data preparation time cut from 2 weeks to 3 minutes.
- Expanded test scenario coverage by 4x using AI-synthesized edge-case patient records.
Capabilities this relies on
- generative design
- workflow orchestration
- anomaly detection
- risk scoring
- system connectors
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