6 min read
AI Governance Frameworks: Building Responsible AI at Scale
Effective AI governance enables innovation while managing risk. Let’s explore practical frameworks for enterprise AI governance.
Governance Framework Overview
AI Governance Pillars:
Strategy & Oversight
├── AI Strategy alignment
├── Executive sponsorship
└── Governance committee
Policies & Standards
├── Acceptable use policy
├── Development standards
└── Vendor requirements
Processes & Controls
├── Risk assessment
├── Approval workflows
└── Monitoring
Technology & Tools
├── Governance platform
├── Audit logging
└── Compliance automationImplementing AI Governance
Governance Structure
from dataclasses import dataclass
from typing import List
from enum import Enum
class ApprovalLevel(Enum):
AUTO = "auto"
TEAM_LEAD = "team_lead"
GOVERNANCE_COMMITTEE = "governance_committee"
EXECUTIVE = "executive"
@dataclass
class AIGovernanceStructure:
"""Define AI governance organizational structure."""
roles = {
"ai_governance_committee": {
"composition": [
"Chief Data Officer (Chair)",
"Chief Technology Officer",
"Chief Risk Officer",
"Legal/Compliance Lead",
"Business Unit Representatives",
"Data Privacy Officer"
],
"responsibilities": [
"Set AI strategy and policies",
"Approve high-risk AI projects",
"Review AI incidents",
"Oversee compliance"
],
"meeting_frequency": "Monthly"
},
"ai_platform_team": {
"composition": [
"AI Platform Lead",
"ML Engineers",
"MLOps Engineers",
"Security Engineers"
],
"responsibilities": [
"Maintain AI platform",
"Implement guardrails",
"Support AI projects",
"Monitor AI systems"
]
},
"ai_ethics_board": {
"composition": [
"External ethics advisor",
"Internal ethics lead",
"Diverse stakeholder representatives"
],
"responsibilities": [
"Ethical review of AI use cases",
"Bias assessment",
"Societal impact evaluation"
]
}
}
def get_approval_level(self, project: dict) -> ApprovalLevel:
"""Determine required approval level."""
risk_score = project.get("risk_score", 0)
business_impact = project.get("business_impact", "low")
if risk_score > 80 or business_impact == "critical":
return ApprovalLevel.EXECUTIVE
if risk_score > 50 or business_impact == "high":
return ApprovalLevel.GOVERNANCE_COMMITTEE
if risk_score > 20 or business_impact == "medium":
return ApprovalLevel.TEAM_LEAD
return ApprovalLevel.AUTOAI Policy Framework
class AIPolicyFramework:
"""Define and enforce AI policies."""
policies = {
"acceptable_use": {
"allowed": [
"Augmenting human decision-making",
"Automating repetitive tasks",
"Improving customer experience",
"Data analysis and insights"
],
"prohibited": [
"Autonomous decisions affecting employment",
"Social scoring of customers",
"Surveillance without consent",
"Generating misleading content"
],
"requires_approval": [
"Customer-facing AI",
"Decision automation",
"Processing sensitive data",
"Using biometric data"
]
},
"data_use": {
"allowed_data_types": [
"Aggregated analytics",
"Anonymized datasets",
"Consented personal data"
],
"prohibited_data_types": [
"Data without proper consent",
"Illegally obtained data",
"Children's data (without guardian consent)"
],
"requirements": [
"Data provenance documentation",
"Privacy impact assessment",
"Data retention limits"
]
},
"model_development": {
"requirements": [
"Documented training process",
"Bias evaluation",
"Performance benchmarks",
"Security review"
],
"approval_gates": [
"Design review",
"Data review",
"Model review",
"Deployment review"
]
},
"vendor_ai": {
"requirements": [
"Vendor AI assessment",
"Data processing agreement",
"Security certification",
"Audit rights"
],
"prohibited_vendors": [
"Vendors without security certification",
"Vendors in restricted jurisdictions"
]
}
}
def check_compliance(self, project: dict) -> dict:
"""Check project against policies."""
violations = []
warnings = []
# Check acceptable use
for prohibited in self.policies["acceptable_use"]["prohibited"]:
if prohibited.lower() in project.get("purpose", "").lower():
violations.append(f"Prohibited use case: {prohibited}")
# Check data use
for data_type in project.get("data_types", []):
if data_type in self.policies["data_use"]["prohibited_data_types"]:
violations.append(f"Prohibited data type: {data_type}")
# Check if approval needed
for approval_case in self.policies["acceptable_use"]["requires_approval"]:
if approval_case.lower() in project.get("description", "").lower():
warnings.append(f"Requires approval: {approval_case}")
return {
"compliant": len(violations) == 0,
"violations": violations,
"warnings": warnings,
"approval_required": len(warnings) > 0
}Risk Assessment Process
class AIRiskAssessment:
"""Structured AI risk assessment."""
def assess_risk(self, project: dict) -> dict:
"""Comprehensive risk assessment."""
risk_dimensions = {
"data_risk": self.assess_data_risk(project),
"model_risk": self.assess_model_risk(project),
"deployment_risk": self.assess_deployment_risk(project),
"compliance_risk": self.assess_compliance_risk(project),
"ethical_risk": self.assess_ethical_risk(project)
}
# Calculate overall risk score
weights = {
"data_risk": 0.25,
"model_risk": 0.20,
"deployment_risk": 0.15,
"compliance_risk": 0.25,
"ethical_risk": 0.15
}
overall_score = sum(
risk_dimensions[dim]["score"] * weights[dim]
for dim in risk_dimensions
)
# Determine risk level
if overall_score > 70:
risk_level = "high"
elif overall_score > 40:
risk_level = "medium"
else:
risk_level = "low"
return {
"overall_score": overall_score,
"risk_level": risk_level,
"dimensions": risk_dimensions,
"mitigations_required": self.get_required_mitigations(risk_dimensions),
"approval_level": self.determine_approval_level(risk_level)
}
def assess_data_risk(self, project: dict) -> dict:
"""Assess data-related risks."""
score = 0
factors = []
# Check for PII
if "pii" in project.get("data_types", []):
score += 30
factors.append("Contains PII")
# Check for sensitive categories
sensitive = ["health", "financial", "biometric"]
for s in sensitive:
if s in project.get("data_types", []):
score += 20
factors.append(f"Contains {s} data")
# Check data volume
if project.get("data_volume", 0) > 1000000:
score += 10
factors.append("Large data volume")
return {"score": min(score, 100), "factors": factors}
def assess_ethical_risk(self, project: dict) -> dict:
"""Assess ethical risks."""
score = 0
factors = []
# Check for decision impact
if project.get("decision_impact") == "high":
score += 40
factors.append("High-impact decisions on individuals")
# Check for fairness considerations
if project.get("affects_protected_groups"):
score += 30
factors.append("Affects protected groups")
# Check for transparency
if not project.get("explainable"):
score += 20
factors.append("Limited explainability")
return {"score": min(score, 100), "factors": factors}Approval Workflow
class AIApprovalWorkflow:
"""Manage AI project approval workflows."""
def __init__(self):
self.pending_approvals = {}
async def submit_for_approval(self, project: dict) -> dict:
"""Submit AI project for approval."""
# Run risk assessment
risk = AIRiskAssessment().assess_risk(project)
# Check policy compliance
compliance = AIPolicyFramework().check_compliance(project)
if not compliance["compliant"]:
return {
"status": "rejected",
"reason": "Policy violations",
"violations": compliance["violations"]
}
# Determine approval path
approval_level = risk["approval_level"]
if approval_level == ApprovalLevel.AUTO:
return {
"status": "approved",
"approval_level": "auto",
"conditions": risk.get("mitigations_required", [])
}
# Create approval request
request_id = self.create_approval_request(project, risk)
# Notify approvers
await self.notify_approvers(request_id, approval_level)
return {
"status": "pending",
"request_id": request_id,
"approval_level": approval_level.value,
"expected_turnaround": self.get_sla(approval_level)
}
async def process_approval(
self,
request_id: str,
decision: str,
approver: str,
comments: str = None
) -> dict:
"""Process approval decision."""
request = self.pending_approvals.get(request_id)
if not request:
raise ValueError(f"Request {request_id} not found")
# Record decision
request["decision"] = {
"status": decision,
"approver": approver,
"timestamp": datetime.now().isoformat(),
"comments": comments
}
if decision == "approved":
# Check if more approvals needed
if self.requires_additional_approval(request):
await self.escalate(request)
return {"status": "escalated"}
# Finalize approval
return await self.finalize_approval(request)
elif decision == "rejected":
return {
"status": "rejected",
"reason": comments,
"appeal_available": True
}
elif decision == "conditional":
return {
"status": "conditional_approval",
"conditions": comments,
"deadline": self.calculate_condition_deadline()
}Monitoring and Continuous Governance
class AIGovernanceMonitoring:
"""Continuous monitoring for AI governance."""
def __init__(self):
self.metrics = {}
async def monitor_ai_systems(self):
"""Continuous monitoring of deployed AI systems."""
while True:
for system_id, system in self.get_active_systems().items():
# Check compliance
compliance = await self.check_runtime_compliance(system)
# Check performance drift
drift = await self.check_model_drift(system)
# Check for incidents
incidents = await self.check_incidents(system)
# Generate alerts if needed
if not compliance["compliant"]:
await self.alert("compliance_violation", system_id, compliance)
if drift["significant"]:
await self.alert("model_drift", system_id, drift)
if incidents:
await self.alert("incidents", system_id, incidents)
await asyncio.sleep(300) # Check every 5 minutes
def generate_governance_report(self) -> dict:
"""Generate governance status report."""
return {
"report_date": datetime.now().isoformat(),
"total_ai_systems": len(self.get_active_systems()),
"compliance_status": {
"compliant": self.count_compliant(),
"non_compliant": self.count_non_compliant(),
"pending_review": self.count_pending()
},
"risk_distribution": self.get_risk_distribution(),
"incidents_this_month": self.count_incidents(),
"pending_approvals": len(self.pending_approvals),
"recommendations": self.generate_recommendations()
}AI governance is not bureaucracy - it’s enablement with guardrails. Build governance that enables innovation while managing risk appropriately.