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Your developers, testers, and AI agents work on realistic data that no longer identifies anyone, de-identified inside your own infrastructure.
Claims systems, RCM products, analytics platforms, and AI agents all need production-like data to build and test against. Copying production into development, QA, and demo environments puts PHI where controls are thinnest, and many client contracts forbid it.
Hand-built test data is slow to make and misses the edge cases real data carries. AI widens the exposure: every agent and model that touches your data needs rules for what it can see, who approves what it does, and what gets logged.
Pacca AI Security & Governance runs across relational and cloud NoSQL databases, X12 and HL7 messages, and flat, XML, and JSON files, in batch or in real time.
Take a representative slice of production with its relationships intact, sized for the test coverage you need.
Each PHI and PII field gets the method that fits it. De-identification is irreversible and can run in place.
Synthetic records in the same formats fill the gaps production leaves, from X12 and HL7 to proprietary files.
Teams pull de-identified data on demand from a self-service portal, and every run leaves a report and a log.
Set the method field by field, and the relationships your systems depend on survive.
Substitution by lookup, substitution by range, scrambling, masking, encryption, and substitution by constant value, set field by field.
Keys and relationships hold across tables and files, so applications and reports run on de-identified data as they do on production.
One patient stays one de-identified patient across every database, claim file, and HL7 message, so longitudinal testing and analytics still work.
Generate high volumes of realistic X12, HL7, database, and proprietary-format records to test ingestion pipelines at scale.
Teams request data from a self-service interface, and de-identified data stays in sync across databases and files.
Permissions decide which people and agents reach which data, approvals gate sensitive actions, guardrails block out-of-scope ones, and every action lands in an audit log.
Emids configures the tool from your schemas and synthetic samples, so your records stay where they are.
Installs on your own servers or cloud and keeps no copy of the sensitive values it replaces.
De-identification runs one way. Access controls, encryption, and audit trails cover the tool itself, in line with the HIPAA Security Rule.
Supports both HIPAA methods: Safe Harbor, which removes the 18 specified identifiers, and Expert Determination, in which a qualified expert you engage assesses re-identification risk.
Irreversible anonymization supports GDPR and CCPA requirements for using personal data in development, testing, and research.
Both teams de-identified the data they had, then generated the data they lacked.
Test data slowed every integration of its RCM products, and contracts barred real PHI from lower environments. Six methods now de-identify its databases, EDI files, and HL7 messages, and real PHI no longer reaches development or test.
High volumes of X12, HL7, database, and proprietary-format data test its ingestion pipelines. Test coverage rose 25%.
Emids installs the tool, de-identifies your sources batch by batch with your sign-off on each, and hands over documentation and training so your team runs it from there.
Databases with up to 75 tables and 50 PHI fields, or one file type with up to 10 PHI or PII fields.
Databases with up to 150 tables and 100 PHI fields, or up to three file types with up to 25 PHI or PII fields.
Databases with more than 150 tables, or up to five file types, with patient matching across sources.
Tell us which databases, files, and message types hold PHI, and we’ll size the work and walk you through a demo.
