AI Governance and Compliance

┌─────────────────────────────────────────┐
│           Governance Dashboard            │
│  (risk registry, compliance status,      │
│   audit trails, model inventory)         │
├─────────────────────────────────────────┤
│           MLOps Platform                  │
│  (model registry, experiment tracking,   │
│   deployment pipelines, monitoring)      │
├─────────────────────────────────────────┤
│           Technical Controls              │
│  (bias detection, explainability,        │
│   data lineage, access controls)         │
└─────────────────────────────────────────┘

import mlflow

# Register model with governance metadata
with mlflow.start_run():
    mlflow.log_param("training_data_version", "v2.3")
    mlflow.log_param("data_lineage", "s3://data/training/v2.3")
    mlflow.log_param("bias_assessment", "passed")
    mlflow.log_param("risk_classification", "high-risk")
    mlflow.log_param("human_oversight", "required")

    mlflow.log_metric("demographic_parity", 0.95)
    mlflow.log_metric("equalized_odds", 0.92)

    mlflow.log_artifact("model_card.md")
    mlflow.log_artifact("data_sheet.pdf")
    mlflow.log_artifact("impact_assessment.pdf")

    mlflow.sklearn.log_model(model, "model",
        registered_model_name="credit-scoring-v3")

from fairlearn.metrics import MetricFrame, selection_rate

def monitor_model_fairness(predictions, sensitive_features):
    """Run fairness checks on model predictions."""
    metric_frame = MetricFrame(
        metrics={
            "selection_rate": selection_rate,
            "accuracy": accuracy_score,
        },
        y_true=actual_outcomes,
        y_pred=predictions,
        sensitive_features=sensitive_features
    )

    # Check demographic parity
    disparity = metric_frame.difference()

    if disparity["selection_rate"] > 0.1:  # 10% threshold
        alert_governance_team(
            model="credit-scoring-v3",
            metric="demographic_parity",
            disparity=disparity["selection_rate"]
        )

# Kubernetes: AI audit logging
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-audit-logger
spec:
  template:
    spec:
      containers:
      - name: logger
        image: governance/audit-logger:latest
        env:
        - name: LOG_LEVEL
          value: "ALL"  # Every prediction logged
        - name: RETENTION_DAYS
          value: "2555"  # 7 years for financial services
        - name: STORAGE
          value: "s3://ai-audit-logs/"