# Security Policy for Model Operations Module

## Overview

The Model Ops module handles ML model lifecycle operations including training, deployment, versioning, and monitoring. Security is essential as this module manages valuable model assets and potentially sensitive training data.

## Security Considerations

### Model Asset Protection

1. **Model Storage Security**: Store models in secure locations with access controls.
2. **Version Control**: Track all model versions with integrity verification.
3. **Access Logging**: Log all access to model files and artifacts.
4. **Encryption at Rest**: Encrypt sensitive models when stored.

### Training Security

1. **Data Source Validation**: Verify training data sources before use.
2. **Training Environment Isolation**: Isolate training environments from production.
3. **Resource Quotas**: Limit compute resources to prevent abuse.
4. **Checkpoint Security**: Protect training checkpoints and intermediate artifacts.

### Deployment Security

1. **Model Verification**: Verify model integrity before deployment.
2. **Rollback Capability**: Maintain ability to quickly rollback to previous versions.
3. **Environment Separation**: Separate staging from production deployments.
4. **Deployment Authorization**: Require explicit authorization for production deployments.

### Inference Security

1. **Input Validation**: Validate inference inputs before processing.
2. **Output Sanitization**: Sanitize model outputs appropriately.
3. **Rate Limiting**: Implement rate limits on inference endpoints.
4. **Monitoring**: Monitor for anomalous inference patterns.

## Threat Model

| Threat | Impact | Mitigation |
|--------|--------|------------|
| Model theft | IP loss | Access controls, encryption, watermarking |
| Model poisoning | Corrupted outputs | Training data validation, integrity checks |
| Inference abuse | Resource exhaustion | Rate limiting, quotas |
| Unauthorized deployment | Production issues | Deployment authorization, approval workflows |
| Data leakage | Privacy violation | Data sanitization, access logging |
| Adversarial attacks | Incorrect predictions | Input validation, adversarial detection |

## Secure Implementation Patterns

```python
# Example: Secure model deployment
class SecureModelDeployer:
    def __init__(self, registry: ModelRegistry):
        self.registry = registry
        self._authorized_deployers = set()

    def deploy_model(
        self,
        model_id: str,
        version: str,
        environment: str,
        deployer: User
    ) -> DeploymentResult:
        """Securely deploy a model to an environment."""
        # Authorization check
        if not self._is_authorized(deployer, environment):
            audit_log.record_unauthorized_deployment(deployer, model_id)
            raise AuthorizationError("Not authorized to deploy")

        # Verify model exists and is valid
        model = self.registry.get_model(model_id, version)
        if not model:
            raise ModelNotFoundError(f"Model {model_id}:{version} not found")

        # Integrity verification
        if not self._verify_integrity(model):
            raise IntegrityError("Model integrity check failed")

        # Production requires additional approval
        if environment == "production":
            if not self._has_production_approval(model_id, version):
                raise ApprovalRequiredError("Production deployment requires approval")

        # Perform deployment
        result = self._deploy(model, environment)

        # Audit log
        audit_log.record_deployment(
            model_id=model_id,
            version=version,
            environment=environment,
            deployer=deployer.id,
            status=result.status
        )

        return result
```

## Model Registry Security

1. **Authentication**: Require authentication for registry access
2. **Authorization**: Implement role-based access to models
3. **Audit Logging**: Log all registry operations
4. **Immutable Versions**: Make published model versions immutable

## Compliance

- Maintain model lineage and provenance
- Track training data usage for compliance
- Implement model governance policies
- Support model explainability requirements

## Vulnerability Reporting

Report security vulnerabilities via the main project's security reporting process.
