Governance-as-Code — Causal Compliance Layer

Counterfactual fairness verification: generates counterfactual inputs (race, gender, age swapped) and validates that model outputs remain invariant — proving decisions are causally fair, not just statistically balanced.

Proxy bias interception: detects when protected attributes leak through correlated features (zip code as race proxy, name as gender proxy) using causal graph analysis. Blocks or flags outputs before they reach production.

Model output governance: sits as an interception layer between any enterprise AI model (LLMs, credit scoring, fraud detection, clinical decision support) and downstream consumers. Enforces policy gates on every prediction.

Multi-framework regulatory compliance: automated reporting for EU AI Act Article 9 (risk management), NIST AI RMF, SEC AI disclosure requirements, FDA PCCP (Predetermined Change Control Plans), and ECOA/fair lending mandates.

Immutable audit trail generation: cryptographically signed logs of every model decision, fairness check result, counterfactual test, and governance action. Produces regulator-ready evidence packages for examinations and audits.

Real-time bias monitoring dashboard: continuous statistical monitoring of model outputs across all protected classes with drift detection, alert thresholds, and automatic remediation triggers when fairness metrics degrade.