AI Governance Frameworks: Building Responsible AI Systems

AI governance is essential for enterprise AI adoption. Here’s how to implement comprehensive governance frameworks.

AI Governance Implementation

from dataclasses import dataclass
from typing import List, Optional
from enum import Enum

class RiskLevel(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"

@dataclass
class AISystemRegistration:
    name: str
    purpose: str
    owner: str
    data_sources: List[str]
    model_type: str
    risk_level: RiskLevel
    approved: bool = False

class AIGovernanceFramework:
    def __init__(self):
        self.registry = AISystemRegistry()
        self.policy_engine = PolicyEngine()
        self.audit_log = AuditLog()

    async def register_system(self, registration: AISystemRegistration) -> str:
        """Register AI system for governance tracking."""
        # Validate registration
        validation = await self.validate_registration(registration)
        if not validation.passed:
            raise GovernanceError(validation.errors)

        # Assess risk
        risk_assessment = await self.assess_risk(registration)
        registration.risk_level = risk_assessment.level

        # Apply policies based on risk
        policies = self.policy_engine.get_policies(registration.risk_level)
        registration.required_controls = policies

        # Register system
        system_id = self.registry.register(registration)
        self.audit_log.log("system_registered", system_id, registration)

        return system_id

    async def assess_risk(self, registration: AISystemRegistration) -> RiskAssessment:
        """Assess AI system risk level."""
        risk_factors = []

        # Data sensitivity
        if any(d in registration.data_sources for d in ["pii", "phi", "financial"]):
            risk_factors.append(RiskFactor("data_sensitivity", "high"))

        # Decision impact
        if registration.purpose in ["hiring", "lending", "healthcare"]:
            risk_factors.append(RiskFactor("decision_impact", "high"))

        # Model type
        if registration.model_type == "generative":
            risk_factors.append(RiskFactor("model_type", "medium"))

        return RiskAssessment.calculate(risk_factors)

    async def enforce_policies(self, system_id: str, action: str, context: dict) -> bool:
        """Enforce governance policies for AI action."""
        system = self.registry.get(system_id)
        policies = self.policy_engine.get_policies(system.risk_level)

        for policy in policies:
            result = await policy.evaluate(action, context)
            if not result.allowed:
                self.audit_log.log("policy_violation", system_id, {
                    "policy": policy.name,
                    "action": action,
                    "reason": result.reason
                })
                return False

        self.audit_log.log("action_allowed", system_id, {"action": action})
        return True

    async def generate_compliance_report(self, period: str) -> ComplianceReport:
        """Generate compliance report for AI systems."""
        systems = self.registry.get_all()
        report = ComplianceReport(period=period)

        for system in systems:
            audit_entries = self.audit_log.get_entries(system.id, period)
            compliance_status = self.evaluate_compliance(system, audit_entries)
            report.add_system(system, compliance_status)

        return report

Comprehensive AI governance ensures responsible and compliant AI deployment.