AI Governance Frameworks: Building Trust at Scale

As AI deployments scale, governance becomes critical. A robust framework ensures AI is used responsibly, ethically, and in compliance with regulations.

The AI Governance Framework

from dataclasses import dataclass, field
from typing import List, Dict, Optional
from enum import Enum
from datetime import datetime

class RiskLevel(Enum):
    LOW = "Low"
    MEDIUM = "Medium"
    HIGH = "High"
    CRITICAL = "Critical"

class GovernanceDomain(Enum):
    STRATEGY = "Strategy & Oversight"
    RISK = "Risk Management"
    DATA = "Data Governance"
    MODEL = "Model Governance"
    ETHICS = "Ethics & Responsibility"
    OPERATIONS = "Operations & Monitoring"
    COMPLIANCE = "Compliance"

@dataclass
class GovernancePolicy:
    domain: GovernanceDomain
    policy_name: str
    description: str
    requirements: List[str]
    owner: str
    review_frequency: str

@dataclass
class AIGovernanceFramework:
    organization: str
    version: str
    policies: List[GovernancePolicy] = field(default_factory=list)
    risk_matrix: Dict = field(default_factory=dict)

    def add_policy(self, policy: GovernancePolicy):
        self.policies.append(policy)

    def get_policies_by_domain(self, domain: GovernanceDomain) -> List[GovernancePolicy]:
        return [p for p in self.policies if p.domain == domain]

# Create framework
framework = AIGovernanceFramework(
    organization="Enterprise Corp",
    version="1.0"
)

# Add core policies
framework.add_policy(GovernancePolicy(
    domain=GovernanceDomain.STRATEGY,
    policy_name="AI Use Case Approval",
    description="All AI use cases must be approved before development",
    requirements=[
        "Business case documentation",
        "Risk assessment completion",
        "Executive sponsor identified",
        "Success metrics defined",
        "Budget approved"
    ],
    owner="AI Steering Committee",
    review_frequency="Annual"
))

framework.add_policy(GovernancePolicy(
    domain=GovernanceDomain.RISK,
    policy_name="AI Risk Assessment",
    description="Mandatory risk assessment for all AI systems",
    requirements=[
        "Impact assessment completed",
        "Data sensitivity classification",
        "Bias evaluation",
        "Security review",
        "Privacy impact assessment"
    ],
    owner="Risk Management",
    review_frequency="Per deployment"
))

Use Case Approval Process

@dataclass
class AIUseCaseProposal:
    title: str
    description: str
    business_unit: str
    sponsor: str
    proposed_date: datetime
    estimated_investment: float
    expected_benefits: List[str]
    data_requirements: List[str]
    risk_factors: List[str]

class UseCaseApprovalWorkflow:
    STAGES = [
        "Submitted",
        "Initial Review",
        "Risk Assessment",
        "Technical Review",
        "Ethics Review",
        "Final Approval",
        "Approved",
        "Rejected"
    ]

    def __init__(self):
        self.proposals: Dict[str, Dict] = {}

    def submit_proposal(self, proposal: AIUseCaseProposal) -> str:
        """Submit a new use case proposal."""
        import uuid
        proposal_id = str(uuid.uuid4())[:8]

        self.proposals[proposal_id] = {
            "proposal": proposal,
            "stage": "Submitted",
            "reviews": [],
            "submitted_at": datetime.now(),
            "decision": None
        }

        return proposal_id

    def conduct_initial_review(self, proposal_id: str, reviewer: str) -> Dict:
        """Initial screening review."""
        checks = {
            "business_alignment": {
                "question": "Does this align with strategic priorities?",
                "required": True
            },
            "feasibility": {
                "question": "Is this technically feasible?",
                "required": True
            },
            "resource_availability": {
                "question": "Are resources available?",
                "required": True
            },
            "similar_initiatives": {
                "question": "Are there similar existing initiatives?",
                "required": False
            }
        }

        return {
            "proposal_id": proposal_id,
            "stage": "Initial Review",
            "reviewer": reviewer,
            "checks": checks
        }

    def conduct_risk_assessment(self, proposal_id: str) -> Dict:
        """Comprehensive risk assessment."""
        risk_categories = {
            "operational_risk": {
                "factors": [
                    "System availability requirements",
                    "Human oversight needs",
                    "Fallback procedures"
                ],
                "weight": 0.25
            },
            "data_risk": {
                "factors": [
                    "Data sensitivity level",
                    "Data quality concerns",
                    "Data privacy implications"
                ],
                "weight": 0.25
            },
            "model_risk": {
                "factors": [
                    "Accuracy requirements",
                    "Bias potential",
                    "Explainability needs"
                ],
                "weight": 0.25
            },
            "reputational_risk": {
                "factors": [
                    "Customer-facing?",
                    "Decision impact on people",
                    "Public visibility"
                ],
                "weight": 0.25
            }
        }

        return {
            "proposal_id": proposal_id,
            "risk_categories": risk_categories,
            "assessment_template": self._generate_assessment_template(risk_categories)
        }

    def _generate_assessment_template(self, categories: Dict) -> str:
        template = "# AI Risk Assessment\n\n"
        for category, details in categories.items():
            template += f"## {category.replace('_', ' ').title()}\n\n"
            for factor in details["factors"]:
                template += f"- [ ] {factor}: [Score 1-5] [Comments]\n"
            template += "\n"
        return template

Model Governance

@dataclass
class ModelRegistry:
    """Central registry for all AI models."""
    models: Dict[str, Dict] = field(default_factory=dict)

    def register_model(
        self,
        model_id: str,
        model_name: str,
        model_type: str,
        version: str,
        owner: str,
        use_cases: List[str],
        risk_level: RiskLevel,
        documentation_url: str
    ):
        """Register a model in the governance registry."""
        self.models[model_id] = {
            "name": model_name,
            "type": model_type,
            "version": version,
            "owner": owner,
            "use_cases": use_cases,
            "risk_level": risk_level,
            "documentation": documentation_url,
            "registered_at": datetime.now(),
            "status": "Active",
            "reviews": [],
            "incidents": []
        }

    def schedule_review(self, model_id: str, review_type: str, due_date: datetime):
        """Schedule a model review."""
        if model_id in self.models:
            self.models[model_id]["reviews"].append({
                "type": review_type,
                "due_date": due_date,
                "status": "Scheduled"
            })

    def report_incident(self, model_id: str, incident_description: str, severity: str):
        """Report an incident related to a model."""
        if model_id in self.models:
            self.models[model_id]["incidents"].append({
                "description": incident_description,
                "severity": severity,
                "reported_at": datetime.now(),
                "status": "Open"
            })

# Example usage
registry = ModelRegistry()

registry.register_model(
    model_id="gpt4-customer-service",
    model_name="Customer Service Assistant",
    model_type="LLM (GPT-4)",
    version="1.2.0",
    owner="Customer Experience Team",
    use_cases=["Customer inquiry response", "FAQ generation"],
    risk_level=RiskLevel.MEDIUM,
    documentation_url="https://docs.company.com/ai/customer-service"
)

Monitoring and Compliance

class GovernanceMonitoring:
    """Monitor AI systems for governance compliance."""

    def __init__(self, framework: AIGovernanceFramework):
        self.framework = framework
        self.alerts: List[Dict] = []

    def check_compliance(self, model_id: str, model_data: Dict) -> Dict:
        """Check a model against governance requirements."""
        compliance_results = {
            "model_id": model_id,
            "checked_at": datetime.now(),
            "findings": []
        }

        # Check documentation
        if not model_data.get("documentation"):
            compliance_results["findings"].append({
                "category": "Documentation",
                "severity": "Medium",
                "finding": "Model documentation missing"
            })

        # Check review status
        reviews = model_data.get("reviews", [])
        overdue_reviews = [r for r in reviews if r["due_date"] < datetime.now() and r["status"] == "Scheduled"]
        if overdue_reviews:
            compliance_results["findings"].append({
                "category": "Review",
                "severity": "High",
                "finding": f"{len(overdue_reviews)} overdue reviews"
            })

        # Check incident management
        open_incidents = [i for i in model_data.get("incidents", []) if i["status"] == "Open"]
        if open_incidents:
            compliance_results["findings"].append({
                "category": "Incidents",
                "severity": "High",
                "finding": f"{len(open_incidents)} unresolved incidents"
            })

        compliance_results["compliant"] = len(compliance_results["findings"]) == 0

        return compliance_results

    def generate_governance_report(self, registry: ModelRegistry) -> str:
        """Generate organization-wide governance report."""
        report = "# AI Governance Report\n\n"
        report += f"Generated: {datetime.now().strftime('%Y-%m-%d')}\n\n"

        report += "## Model Inventory\n\n"
        report += f"Total Models: {len(registry.models)}\n\n"

        # By risk level
        risk_counts = {}
        for model in registry.models.values():
            level = model["risk_level"].value
            risk_counts[level] = risk_counts.get(level, 0) + 1

        report += "### By Risk Level\n"
        for level, count in risk_counts.items():
            report += f"- {level}: {count}\n"

        # Compliance summary
        report += "\n## Compliance Summary\n\n"
        compliant = sum(1 for m in registry.models.values() if not m.get("incidents"))
        report += f"Models in Compliance: {compliant}/{len(registry.models)}\n"

        return report

Tomorrow, we’ll explore AI risk management in depth!