The Compliance Hub — Regulatory & Insurance Alignment
"Safety is no longer a self-attestation. It is a technical requirement."

 
The Cost of Manual Compliance
The following projections represent the estimated annual manual labor required to maintain safety benchmarks for an AI application deployed without independent middleware, assuming a standard update cycle of 1–2 releases per week.

Global Regulatory Mapping

SASI is engineered to map directly to the requirements of the world’s most stringent regulatory frameworks. 

🇪🇺 EU AI Act Compliance
SASI helps developers navigate the complexities of the EU AI Act, particularly regarding emotion recognition and transparency.

Article 5 Alignment: Features an "EU-Compliant Mode" that disables emotional state detection in education (Student Mode) while maintaining safety-critical crisis detection.  

Article 13 Transparency: Provides structured explanation_components to help partners generate child-friendly transparency documentation.  
 
 
🇺🇸 US Healthcare & Privacy (HIPAA / FDA)
For clinical and telehealth applications, SASI provides the "Hard Safety" floor required for patient safety. 

HIPAA Safe Harbor: Automated redaction of all 18 PHI identifiers with a mandatory 7-year audit retention.  

FDA Readiness: Generates turn-by-turn decision traces and reconstruction artifacts required for high-assurance medical software audits.  
 
 
🧒 Children & Education (COPPA / FERPA)
SASI’s child and student modes are designed for the most vulnerable users. 

COPPA Compliance: Enforces Maximum PII redaction (including school/location) that cannot be disabled by administrators.  

FERPA Alignment: Provides 7-year audit logs and academic_concern flags for educational integrity.  

General Compliance

Insurable AI Infrastructure:
Insurance providers are increasingly requiring independent safety verification before underwriting AI liability. SASI provides: 

Deterministic Governance: Proof that safety logic is independent of the "black box" LLM.  

Audit-ability: Every decision includes a decision_tree_path and action_rationale to prove why a specific safety action was taken.  

Model Agnostic Insurance: Your safety profile stays constant even if you switch model providers, protecting your risk posture over time.