PHI Protection for AI Workflows
How we keep PHI out of external models and aligned with HIPAA
The PHI Risk in AI Tools
What happens when staff paste PHI into external AI models
Our PHI Protection Approach
Detect → Cleanse → Send → Rehydrate
Detect
Automatic scanning for all 18 HIPAA identifiers in real-time as users type or upload content. Technical: Pattern matching, NER models, contextual analysis.
Cleanse
Replace PHI with randomized placeholders while preserving clinical context and meaning. Technical: Token replacement, semantic preservation, reversible mapping.
Send
De-identified content sent to AI model (ChatGPT, Claude, etc.) with zero PHI exposure. Technical: Encrypted transport, no PHI in logs, no training corpus exposure.
Rehydrate
AI response returned to user with original PHI restored in secure environment. Technical: Local rehydration, no PHI stored, audit trail maintained.
Technical Controls
De-identification
All 18 HIPAA identifiers detected: names, dates, addresses, MRNs, SSNs, phone numbers, emails, account numbers, biometric identifiers, device IDs, and any unique identifying information.
Placeholder Logic
Randomized, non-reversible tokens that maintain clinical context and preserve semantic meaning. Consistent within session, no PHI in external systems.
Transport & Storage Security
End-to-end encryption (TLS 1.3), zero PHI in vendor logs, no training corpus exposure, data residency controls, and complete audit trail.
Organizational Controls
Technology is only part of the solution — governance requires people and process
Compliance Alignment
HIPAA
PHI protection, BAAs, safeguards
SOC 2 Type II
Security, availability, confidentiality
HITRUST
Healthcare information security framework
State Privacy Laws
CCPA, state-specific regulations
For Your Security & Compliance Team
We understand your team needs detailed documentation. Available on request: Architecture diagrams & data flow maps, Security controls matrix, PHI de-identification methodology, Penetration testing results, SOC 2 Type II report, Business Associate Agreement (BAA), Incident response procedures, Vendor risk assessment questionnaire, HIPAA alignment documentation, Data processing addendum (DPA), Audit log specifications, Encryption standards & key management.
During the 6-Week Governance Pilot, we schedule dedicated sessions with your security and compliance teams to review technical controls, answer questions, and provide all documentation needed for internal approvals.
We don't ask you to "trust us" — we show you exactly how PHI protection works and provide evidence that meets your standards.
Frequently Asked Questions
Does PHI protection work with all AI models?
Yes. Our approach is model-agnostic — it works with ChatGPT, Claude, Gemini, or any AI tool your staff wants to use. The de-identification happens before content reaches the external model.
What about false positives or missed PHI?
Our system uses multiple detection layers (pattern matching, NER models, contextual analysis) to minimize false positives and negatives. We also provide user feedback mechanisms and ongoing tuning based on your specific workflows.
Can we get a BAA with the AI vendors directly?
Some vendors (like OpenAI Enterprise) offer BAAs, but most don't. Even with a BAA, you still need de-identification as a safeguard. Our approach gives you defense-in-depth: PHI never reaches the vendor in the first place.
How do you handle edge cases like rare diseases or unique identifiers?
Our system learns from your environment during the pilot. We tune detection rules for your specific context (e.g., department names, facility identifiers, rare conditions) to ensure comprehensive coverage.
What if a user tries to bypass the system?
Technical controls (enforced at the network/application layer) prevent circumvention. Plus, governance training and monitoring create accountability. We also provide usage analytics to detect workarounds.
Ready to Protect PHI in AI Workflows?
Schedule a security review or start with a Shadow AI Risk Check.