Valiron

AI Regulatory Compliance Validation for Python

Valiron is a Python library that helps ML engineers and data scientists validate their AI/ML models against global regulatory frameworks — before deployment.

Problem

Deploying AI in regulated industries (healthcare, finance, HR) requires compliance with frameworks like the EU AI Act, FDA AI/ML SaMD guidance, CDSCO MDSW, and RBI ML Model Risk guidelines. Manual compliance checks are slow, inconsistent, and expensive.

Solution

Valiron automates compliance validation with a simple API:

import valiron

result = valiron.evaluate(
    model=my_sklearn_model,
    X_test=X_test,
    y_test=y_test,
    regulation="eu_ai_act",
    use_case="medical_diagnosis"
)

report = valiron.report(result, format="pdf")

Compliance Coverage

Quickstart

pip install valiron

import valiron
from sklearn.ensemble import RandomForestClassifier

# Train your model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Validate compliance
result = valiron.evaluate(
    model=model,
    X_test=X_test,
    y_test=y_test,
    regulation="cdsco_mdsw",
    use_case="diagnostic_aid",
    sensitive_features=["age", "gender"]
)

# Check results
print(result.compliant)          # True/False
print(result.score)              # 0.0 - 1.0
print(result.failing_checks)    # list of failed requirements

# Generate report
valiron.report(result, format="html", output="compliance_report.html")

Supported Frameworks

• scikit-learn
• PyTorch
• ONNX
• Raw predictions (numpy arrays)

Data Privacy Architecture

• No data leaves your machine. All validation runs locally.
• No model weights are transmitted.
• Reports are generated on-device.
• Optional: anonymize test data before validation with valiron.anonymize().

Roadmap

• ISO 42001 (AI Management Systems)
• HIPAA AI compliance checks
• SEBI AI governance framework
• CI/CD integration (GitHub Actions, GitLab CI)
• VS Code extension

License

MIT — see LICENSE