Model Governance
Comprehensive governance framework for managing LLM models, ensuring compliance, security, and responsible AI practices across your organization.
Overview
Model Governance in LLMOps provides the framework and tools needed to ensure responsible, compliant, and secure use of LLM models across your organization. It encompasses policies, procedures, and controls that govern model development, deployment, and usage.
Governance Framework
Core Principles
- Transparency - Clear visibility into model behavior and decisions
- Accountability - Defined roles and responsibilities for model management
- Compliance - Adherence to regulations and industry standards
- Security - Protection of data and prevention of misuse
- Fairness - Bias detection and mitigation
- Reliability - Consistent and predictable model performance
Governance Policies
Model Approval Process
typescript
// Define model approval workflow
const governance = await ants.llmops.governance
const approvalWorkflow = await governance.createApprovalWorkflow({
name: 'production-model-approval',
stages: [
{
name: 'technical-review',
approvers: ['ml-engineer', 'data-scientist'],
requirements: ['performance-tests', 'security-scan', 'bias-assessment']
},
{
name: 'business-review',
approvers: ['product-manager', 'business-analyst'],
requirements: ['business-case', 'roi-analysis', 'risk-assessment']
},
{
name: 'compliance-review',
approvers: ['compliance-officer', 'legal-team'],
requirements: ['privacy-impact-assessment', 'regulatory-compliance']
}
],
autoApproval: {
conditions: ['low-risk', 'standard-use-case', 'approved-model-family']
}
})
console.log(`Approval workflow created: ${approvalWorkflow.id}`)
Policy Definition
python
# Define governance policies
policy_manager = ants.llmops.policy_manager
# Create data privacy policy
privacy_policy = policy_manager.create_policy({
'name': 'data-privacy-policy',
'category': 'privacy',
'rules': [
{
'name': 'pii-handling',
'description': 'All PII must be detected and redacted',
'enforcement': 'automatic',
'severity': 'high'
},
{
'name': 'data-retention',
'description': 'Model outputs must be retained for audit purposes',
'enforcement': 'automatic',
'retention_period': '7_years'
},
{
'name': 'consent-management',
'description': 'User consent must be obtained for data processing',
'enforcement': 'manual',
'severity': 'medium'
}
]
})
# Create bias and fairness policy
fairness_policy = policy_manager.create_policy({
'name': 'bias-fairness-policy',
'category': 'fairness',
'rules': [
{
'name': 'bias-detection',
'description': 'Models must be tested for bias across protected groups',
'enforcement': 'automatic',
'thresholds': {
'demographic_parity': 0.8,
'equalized_odds': 0.85
}
},
{
'name': 'fairness-monitoring',
'description': 'Continuous monitoring for bias in production',
'enforcement': 'automatic',
'alert_threshold': 0.1
}
]
})
Access Control & Permissions
Role-Based Access Control (RBAC)
typescript
// Define roles and permissions
const rbac = await ants.llmops.rbac
// Create roles
const roles = await Promise.all([
rbac.createRole({
name: 'ml-engineer',
permissions: [
'model:create',
'model:update',
'model:test',
'prompt:create',
'prompt:update'
],
restrictions: {
environments: ['development', 'staging'],
maxModels: 10
}
}),
rbac.createRole({
name: 'data-scientist',
permissions: [
'model:read',
'model:test',
'data:access',
'analytics:view'
],
restrictions: {
dataAccess: 'anonymized-only'
}
}),
rbac.createRole({
name: 'compliance-officer',
permissions: [
'model:approve',
'audit:view',
'policy:manage',
'compliance:report'
],
restrictions: {
approvalRequired: true
}
})
])
console.log('Roles created:', roles.map(r => r.name))
Resource-Level Permissions
python
# Define resource-level permissions
resource_permissions = ants.llmops.resource_permissions
# Model-level permissions
model_permissions = resource_permissions.create({
'resource_type': 'model',
'permissions': {
'customer-support-model': {
'ml-engineer': ['read', 'update', 'test'],
'data-scientist': ['read', 'test'],
'compliance-officer': ['read', 'approve']
},
'financial-model': {
'ml-engineer': ['read'],
'compliance-officer': ['read', 'approve'],
'auditor': ['read']
}
}
})
# Prompt-level permissions
prompt_permissions = resource_permissions.create({
'resource_type': 'prompt',
'permissions': {
'customer-support-classifier': {
'ml-engineer': ['read', 'update'],
'product-manager': ['read']
}
}
})
Compliance Monitoring
Regulatory Compliance
typescript
// Monitor compliance with regulations
const compliance = await ants.llmops.compliance
// GDPR Compliance
const gdprCompliance = await compliance.createComplianceMonitor({
regulation: 'GDPR',
requirements: [
{
name: 'data-minimization',
description: 'Only collect necessary data',
check: 'data-usage-audit',
frequency: 'daily'
},
{
name: 'right-to-erasure',
description: 'Support data deletion requests',
check: 'deletion-capability-test',
frequency: 'weekly'
},
{
name: 'consent-management',
description: 'Track user consent',
check: 'consent-audit',
frequency: 'daily'
}
]
})
// SOX Compliance
const soxCompliance = await compliance.createComplianceMonitor({
regulation: 'SOX',
requirements: [
{
name: 'audit-trail',
description: 'Maintain complete audit trails',
check: 'audit-trail-completeness',
frequency: 'daily'
},
{
name: 'access-controls',
description: 'Implement proper access controls',
check: 'access-control-audit',
frequency: 'weekly'
}
]
})
Automated Compliance Checking
python
# Run automated compliance checks
compliance_checker = ants.llmops.compliance_checker
# Check model compliance
model_compliance = compliance_checker.check_model({
'model_id': 'customer-support-v2',
'regulations': ['gdpr', 'ccpa', 'sox'],
'checks': [
'data-handling',
'access-controls',
'audit-trails',
'bias-assessment'
]
})
print("Model Compliance Results:")
for regulation, results in model_compliance.items():
print(f"\n{regulation.upper()}:")
print(f" Overall Score: {results.score}/100")
print(f" Status: {results.status}")
for check in results.checks:
print(f" {check.name}: {check.status} - {check.description}")
Audit & Reporting
Audit Trail Management
typescript
// Comprehensive audit trail
const audit = await ants.llmops.audit
// Track model changes
const modelAudit = await audit.createAuditTrail({
resourceType: 'model',
resourceId: 'customer-support-v2',
events: [
'create',
'update',
'deploy',
'approve',
'retire'
],
retention: '7_years',
immutable: true
})
// Track prompt changes
const promptAudit = await audit.createAuditTrail({
resourceType: 'prompt',
resourceId: 'customer-support-classifier',
events: [
'create',
'update',
'test',
'deploy'
],
retention: '5_years'
})
// Query audit logs
const auditLogs = await audit.queryAuditLogs({
resourceType: 'model',
resourceId: 'customer-support-v2',
timeRange: 'last_30_days',
events: ['update', 'deploy'],
userId: 'ml-engineer-123'
})
console.log('Audit Logs:', auditLogs.entries)
Compliance Reporting
python
# Generate compliance reports
report_generator = ants.llmops.report_generator
# Generate GDPR compliance report
gdpr_report = report_generator.generate({
'report_type': 'gdpr-compliance',
'period': 'last_quarter',
'scope': 'all-models',
'sections': [
'data-processing-activities',
'consent-management',
'data-subject-rights',
'breach-notifications',
'privacy-impact-assessments'
]
})
print("GDPR Compliance Report:")
print(f"Period: {gdpr_report.period}")
print(f"Overall Compliance: {gdpr_report.overall_score}/100")
print(f"Critical Issues: {gdpr_report.critical_issues}")
print(f"Recommendations: {len(gdpr_report.recommendations)}")
# Generate SOX compliance report
sox_report = report_generator.generate({
'report_type': 'sox-compliance',
'period': 'last_quarter',
'scope': 'financial-models',
'sections': [
'internal-controls',
'audit-trails',
'access-controls',
'change-management'
]
})
Risk Management
Risk Assessment
typescript
// Assess model risks
const riskManager = await ants.llmops.riskManager
const riskAssessment = await riskManager.assessModelRisk({
modelId: 'customer-support-v2',
riskCategories: [
'data-privacy',
'bias-fairness',
'security',
'operational',
'regulatory'
],
assessmentCriteria: {
dataSensitivity: 'high',
userImpact: 'medium',
regulatoryEnvironment: 'strict'
}
})
console.log('Risk Assessment Results:')
console.log(`Overall Risk Score: ${riskAssessment.overallScore}/100`)
console.log(`Risk Level: ${riskAssessment.riskLevel}`)
console.log('Top Risks:', riskAssessment.topRisks)
Risk Mitigation
python
# Implement risk mitigation strategies
risk_mitigation = ants.llmops.risk_mitigation
# Create risk mitigation plan
mitigation_plan = risk_mitigation.create_plan({
'model_id': 'customer-support-v2',
'risks': [
{
'risk': 'data-privacy-breach',
'probability': 'medium',
'impact': 'high',
'mitigation': [
'implement-pii-detection',
'add-data-encryption',
'regular-security-audits'
]
},
{
'risk': 'bias-in-decisions',
'probability': 'low',
'impact': 'medium',
'mitigation': [
'bias-testing-suite',
'continuous-monitoring',
'diverse-training-data'
]
}
]
})
print("Risk Mitigation Plan:")
for risk in mitigation_plan.risks:
print(f"\nRisk: {risk.risk}")
print(f"Mitigation Strategies: {', '.join(risk.mitigation)}")
Quality Assurance
Model Quality Gates
typescript
// Define quality gates for model deployment
const qualityGates = await ants.llmops.qualityGates
const deploymentGate = await qualityGates.createGate({
name: 'production-deployment-gate',
stages: [
{
name: 'performance-gate',
requirements: {
accuracy: { min: 0.90 },
latency: { max: 2000 },
throughput: { min: 100 }
}
},
{
name: 'security-gate',
requirements: {
vulnerabilityScan: 'passed',
penetrationTest: 'passed',
accessControls: 'implemented'
}
},
{
name: 'compliance-gate',
requirements: {
privacyImpactAssessment: 'completed',
biasAssessment: 'passed',
auditTrail: 'configured'
}
}
],
blocking: true
})
console.log(`Quality gate created: ${deploymentGate.id}`)
Continuous Quality Monitoring
python
# Monitor model quality continuously
quality_monitor = ants.llmops.quality_monitor
# Set up quality monitoring
monitoring_config = quality_monitor.setup({
'model_id': 'customer-support-v2',
'metrics': [
'accuracy',
'latency',
'bias_score',
'user_satisfaction',
'error_rate'
],
'thresholds': {
'accuracy': {'min': 0.85, 'alert': 0.80},
'latency': {'max': 3000, 'alert': 2500},
'bias_score': {'max': 0.1, 'alert': 0.08}
},
'alerts': {
'email': ['ml-team@company.com'],
'slack': ['#ml-alerts'],
'pagerduty': ['ml-oncall']
}
})
print("Quality monitoring configured")
print(f"Monitoring {len(monitoring_config.metrics)} metrics")
print(f"Alert channels: {len(monitoring_config.alerts)}")
Best Practices
1. Governance Framework
- Establish clear policies and procedures
- Define roles and responsibilities for all stakeholders
- Implement approval workflows for model changes
- Regular policy reviews and updates
2. Compliance Management
- Map regulatory requirements to technical controls
- Implement automated compliance checking where possible
- Maintain comprehensive audit trails for all activities
- Regular compliance assessments and reporting
3. Risk Management
- Conduct regular risk assessments for all models
- Implement risk mitigation strategies proactively
- Monitor risk indicators continuously
- Update risk profiles as models evolve
4. Quality Assurance
- Implement quality gates at every stage
- Continuous monitoring of model performance
- Regular quality reviews and assessments
- Automated quality checks where possible
5. Access Control
- Principle of least privilege for all access
- Regular access reviews and certifications
- Multi-factor authentication for sensitive operations
- Role-based permissions with regular updates
Integration with Other Components
FinOps Integration
- Cost governance policies and controls
- Budget approval workflows
- ROI tracking and reporting
SRE Integration
- Reliability governance and SLAs
- Incident response procedures
- Performance monitoring and alerting
Security Posture Integration
- Security governance and controls
- Threat detection and response
- Security audit and compliance