Technical Governance Implementation
Governance must be embedded in code to be effective. This article provides concrete implementation patterns for SOPHIA-CODE principles.
Audit Logging Pattern
Every significant AI action should be logged:
@dataclass
class AIDecisionRecord:
decision_id: str
timestamp: datetime
model_version: str
input_hash: str
output_hash: str
confidence_score: float
human_approved: bool
approver_id: Optional[str]
def log_ai_decision(record: AIDecisionRecord):
# Immutable, append-only storage
audit_store.append(record)
# Real-time monitoring
monitor.check_anomalies(record)Approval Workflow Pattern
Tiered automation based on risk:
class DecisionTier(Enum):
AUTOMATED = "automated" # Low risk, high confidence
SUPERVISED = "supervised" # Human review required
APPROVAL = "approval" # Explicit approval required
def route_decision(request: AIRequest) -> DecisionTier:
if request.risk_score > 0.8:
return DecisionTier.APPROVAL
elif request.confidence < 0.9:
return DecisionTier.SUPERVISED
else:
return DecisionTier.AUTOMATEDCircuit Breaker Pattern
Stop when things go wrong:
class AICircuitBreaker:
def __init__(self, threshold: float = 0.1):
self.error_rate = 0.0
self.threshold = threshold
self.state = State.CLOSED
def call(self, func, *args, **kwargs):
if self.state == State.OPEN:
raise CircuitOpenError("AI service paused due to errors")
try:
result = func(*args, **kwargs)
self._record_success()
return result
except Exception as e:
self._record_failure()
raiseImplementation Checklist
- [ ] Audit logging for all AI decisions
- [ ] Confidence thresholds configured
- [ ] Approval workflows implemented
- [ ] Circuit breakers in place
- [ ] Rollback procedures tested
- [ ] Monitoring dashboards deployed
- [ ] Alerting rules configured
- [ ] Incident response documented
These patterns form the technical foundation of SOPHIA-CODE governance.