Unlock clinical data for AI
- with review-ready evidence
So Privacy, Compliance, and Legal can approve AI training, research analytics, and cross-institution collaboration - without PHI leaving your control.
Works for Health Systems and Pharma / Life Sciences
The Deadlock
AI initiatives stall when reviewers can't approve what they can't verify.
Patient data blocked from AI
Why: HIPAA "minimum necessary" + organizational policies restrict ML training. Traditional de-identification often reduces utility below research needs.
Consequence: Teams limited to synthetic data or narrow cohorts. High-value AI use cases remain blocked.
No evidence for reviewers
Why: Compliance, IRB, and regulatory reviewers expect documented rationale. Most projects offer narratives, not evidence.
Consequence: Approvals stall in review cycles. Privacy Officers can't sign off on what they can't measure.
Cross-institution sharing blocked
Why: BAAs, IRB protocols, and data sharing agreements create months of legal review per collaboration.
Consequence: Multi-site research and federated analytics remain aspirational. Partners wait while lawyers negotiate.
Realm enables a repeatable approval path - producing the artifacts each stakeholder can evaluate and sign off.
What You Get
Four deliverables that give reviewers what they need to approve.
Privacy-Transformed Dataset
Clinical data transformed into approved analytics and training outputs - preserving research utility, governed and auditable.
Privacy/Utility Metrics
Quantified trade-offs reviewers can evaluate: residual disclosure risk vs. analytic value - not just assurances.
Attack Validation Results
Documented resistance to re-identification, membership inference, and linkage attacks - evidence that transformation holds.
PAMOLA Evidence Pack v0
Config + lineage + metrics + control mapping - structured for Compliance, IRB, and Legal review.
Evidence Pack v0
- Data-flow summary
- Control mapping (supports HIPAA review)
- BAA alignment support
- Run metadata
- Approvals trail structure
Privacy/Utility Metrics
- Privacy risk indicators
- Utility preservation measures
- Technique comparisons
- Policy-aligned thresholds
Attack Validation
- Attack test plan
- Results summary
- Risk gate outcomes
- Mitigation options
Deployment: All processing runs inside your environment (cloud VPC or on-premises). PHI never leaves your control.
Use Cases
Three scenarios where Realm enables clinical data to drive AI value.
Clinical ML Training - With Review-Ready Evidence
The situation:
Teams can't use real patient data for ML training. Limited to synthetic data or narrow cohorts that don't generalize.
What PAMOLA does:
Transforms data inside your environment with measured privacy. Attack validation proves controlled risk. Delivers privacy-transformed dataset + metrics + validation + Evidence Pack.
Sign-off: Privacy Officer · Compliance · Legal · (IRB if research)
AYITA expansion: Governed clinical knowledge assistant with full decision trace
Multi-Institution Collaboration
The situation:
Multi-site research and federated analytics stall on data sharing agreements. Each institution has different review requirements.
What PAMOLA does:
Same scoped pipeline, tailored Evidence Packs for each institution's review process. Delivers privacy-transformed outputs + per-party Evidence Packs + validation + BAA alignment support.
Sign-off: Privacy · Legal · IRB · Partner compliance teams
AYITA expansion: Governed collaborative analytics without direct data access
Patient Analytics & Insights
The situation:
Teams need insights from patient data but can't access production data. Analytics projects delayed by governance cycles.
What PAMOLA does:
Produces governed analytics-ready datasets with privacy metrics proving utility preserved. Delivers analytics-ready dataset + metrics + validation + review-ready documentation.
Sign-off: Privacy Officer · Compliance · Data Governance · (External auditors if applicable)
AYITA expansion: Natural language queries on approved datasets with full traceability
How It Works
From scoping to deployment in weeks, not quarters.
Scope
Week 1
Define dataset and use case. Identify reviewers (Privacy, Compliance, IRB, Legal). Agree on success criteria and evidence requirements.
Evidence Sprint
Weeks 2-4
PAMOLA transforms data inside your environment. Calculates metrics. Executes attack validation. Assembles Evidence Pack v0.
Deploy & Expand
Week 5+
Submit evidence package to reviewers. Use approved dataset. Optional: expand to AYITA for a governed AI runtime on clinical knowledge.
Compliance Support
Evidence artifacts structured for healthcare regulatory review.
HIPAA Privacy Review Support
Control mapping and documentation structured for HIPAA-governed review. Supports both Safe Harbor and Expert Determination pathways.
Security & Access Controls
Data stays inside your perimeter. Evidence artifacts support security and access control review (RBAC, encryption, audit logging).
Research & BAA Support
Evidence packages structured for data sharing agreements and IRB review. Per-party Evidence Packs for multi-institution collaboration.
Expansion - AYITA Governance Optional
Decision trace and interaction logs available for quality review and audit preparation.
Months
Typical data sharing agreement negotiation time
Industry observation
Get Review-Ready Evidence for Healthcare AI
Privacy-first data transformation enables AI training, research analytics, and cross-institution collaboration - with evidence reviewers can approve.
4-week sprint. Evidence package for reviewers. No PHI leaves your environment.