How regulated teams use Pratvi AI.
The scenarios below are hypothetical workflows that map real regulatory obligations to specific platform modules. They are educational examples — not customer attributions.
Bias monitoring for utilization-management AI
Scenario
A health insurer running AI-assisted prior-authorization decisions needs to demonstrate no disparate impact on protected classes — a core concern under ACA Section 1557 and the NAIC Model Bulletin. Without continuous monitoring, a regression in the underlying model can introduce statistically significant disparities long before a manual review would surface them.
How Pratvi AI maps to it
The Bias Monitor module tracks Disparate Impact Ratio, Statistical Parity Difference, and Equal Opportunity Difference per protected class on every decision, alerting when any metric crosses configured thresholds. Adverse-action notices are auto-generated to satisfy state notification requirements when AI denies care.
SR 11-7 model validation for credit-decisioning AI
Scenario
A bank deploying machine-learning credit models is subject to SR 11-7 model risk management. Each model needs documented conceptual soundness, ongoing monitoring, outcomes analysis, and an independent validation trail.
How Pratvi AI maps to it
The Inventory module captures model documentation. The Compliance Engine maps each model to SR 11-7 obligations. The Audit Trail provides ongoing monitoring evidence. The Executive Dashboard generates the validation report regulators expect.
EU AI Act high-risk system conformity assessment
Scenario
An organization deploying AI in employment, education, law enforcement, or essential services across the EU must classify each system under EU AI Act Annex III, complete a conformity assessment, and maintain Article 14 human-oversight controls.
How Pratvi AI maps to it
The Compliance Engine classifies systems automatically against Annex III categories, generates the technical documentation required by Annex IV, enforces Article 14 human-oversight checkpoints in decision flows, and surfaces Article 52 LLM transparency notice triggers.
21 CFR Part 11 audit trail for clinical-trial AI
Scenario
A pharmaceutical sponsor using AI for adverse-event detection or trial-data integrity must maintain audit trails meeting 21 CFR Part 11 — tamper-evident, time-stamped, and capable of reconstruction during FDA inspection.
How Pratvi AI maps to it
Every AI decision is hash-chained with SHA-256 per Pratvi's Audit Trail module, satisfying Part 11 integrity requirements. Each entry captures user, timestamp, action, before/after state, and the regulatory basis. Exportable in regulator-ready format.
CMMC Level 3 controls for AI on CUI
Scenario
A defense contractor handling Controlled Unclassified Information (CUI) must enforce CMMC L2/L3 controls when using AI that touches CUI — including data segregation, supply-chain provenance, and incident reporting under DFARS 252.204-7012.
How Pratvi AI maps to it
The Security Posture module tracks model supply-chain provenance (base model, fine-tuning datasets, dependency chain). The Inventory module enforces CUI-segregation tags. The Audit Trail meets DFARS 72-hour incident-reporting evidence requirements.
Fair-lending review for AI-driven mortgage origination
Scenario
A mortgage lender using ML for underwriting, pricing, and AVM-based valuation must satisfy HMDA reporting, ECOA Reg B adverse-action notices, and HUD Fair Housing Act protected-class analysis.
How Pratvi AI maps to it
The Bias Monitor measures fair-lending metrics on every decision. The Compliance Engine generates HMDA-formatted reports. The Audit Trail retains records for ECOA Reg B's 25-month requirement. Adverse-action notices auto-populate the specific principal reasons regulators expect.
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