The Architecture of Certain Governance

# Quantum Internal Audit Engine
from dataclasses import dataclass
from enum import Enum
import networkx as nx
import numpy as np

class ControlEffectiveness(Enum):
    DESIGN_EFFECTIVE = "design_effective"
    OPERATING_EFFECTIVE = "operating_effective"
    DEFICIENT = "deficient"
    MATERIAL_WEAKNESS = "material_weakness"

@dataclass
class QuantumControl:
    control_id: str
    risk_coverage: Dict[str, float]  # 0-1 score
    test_results: Dict[str, float]
    dependencies: Set[str]
    quantum_state: np.ndarray  # Superposition
    correlation: Dict[str, float]
    
class QuantumAuditEngine:
    def __init__(self, control_universe: nx.DiGraph):
        self.control_universe = control_universe
        self.controls = self.extract_controls()
        self.risk_engine = QuantumRiskAssessmentEngine()
        self.sampling_engine = QuantumSamplingEngine()
        
    def execute_audit_plan(self) -> Dict:
        # Phase 1: Risk-based Scope
        risk_assessment = self.risk_engine.assess_risks(self.controls)
        audit_scope = self.determine_audit_scope(risk_assessment)
        
        # Phase 2: Quantum Sampling
        sample = self.sampling_engine.select_quantum_sample(
            population=self.get_audit_universe(),
            confidence_level=0.95,
            materiality_threshold=0.05
        )
        
        # Phase 3: Parallel Control Testing
        testing_results = self.test_controls_parallel(sample, audit_scope)
        
        # Phase 4: Deficiency Analysis
        deficiencies = self.analyze_deficiencies(testing_results)
        
        # Phase 5: Root Cause Analysis
        root_causes = self.perform_root_cause_analysis(deficiencies)
        
        return {
            'risk_assessment': risk_assessment,
            'testing_results': testing_results,
            'deficiencies': deficiencies,
            'root_causes': root_causes,
            'recommendations': self.generate_recommendations(deficiencies)
        }
    
    def analyze_deficiencies(self, testing_results: Dict) -> Dict:
        # Quantum pattern recognition for deficiencies
        deficiencies = {}
        
        for group, results in testing_results.items():
            if results.get('status') == 'completed':
                patterns = self.identify_deficiency_patterns(results)
                severity = self.calculate_deficiency_severity(patterns)
                
                deficiencies[group] = {
                    'patterns': patterns,
                    'severity_scores': severity,
                    'materiality': self.assess_materiality(patterns),
                    'confidence': self.calculate_confidence(patterns)
                }
        
        return deficiencies

# Quantum Fraud Detection Engine
import pandas as pd
from sklearn.ensemble import XGBClassifier

class QuantumFraudDetectionEngine:
    def __init__(self, transaction_data, employee_data):
        self.transaction_data = transaction_data
        self.employee_data = employee_data
        self.fraud_models = self.initialize_fraud_models()
        
    def initialize_fraud_models(self) -> Dict:
        return {
            'benford_law': BenfordLawModel(),
            'z_score': ZScoreModel(),
            'cluster_analysis': ClusterAnalysisModel(),
            'network_analysis': NetworkAnalysisModel(),
            'machine_learning': FraudMLModel(
                features=[
                    'transaction_amount',
                    'transaction_frequency',
                    'vendor_concentration',
                    'sod_conflicts',
                    'approval_violations'
                ],
                algorithm='xgboost'
            )
        }
    
    def detect_fraud_patterns(self) -> Dict:
        results = {}
        
        # Benford's Law Analysis
        results['benford'] = self.fraud_models['benford_law'].analyze(
            self.transaction_data['amount']
        )
        
        # Statistical Anomaly Detection
        results['anomalies'] = self.fraud_models['z_score'].detect(
            self.transaction_data, threshold=3.0
        )
        
        # Network Analysis
        results['network'] = self.fraud_models['network_analysis'].analyze(
            self.transaction_data, self.employee_data
        )
        
        # ML Prediction
        results['ml_predictions'] = self.fraud_models['machine_learning'].predict(
            self.transaction_data
        )
        
        return {
            'results': results,
            'risk_scores': self.calculate_fraud_risk_scores(results),
            'priority': self.prioritize_investigations(results)
        }

# Production CCM Platform
class ContinuousControlsMonitoring:
    def __init__(self, system_integrations, control_definitions):
        self.integrations = system_integrations
        self.control_definitions = control_definitions
        self.data_pipeline = self.initialize_data_pipeline()
        
    def initialize_data_pipeline(self) -> DataPipeline:
        return DataPipeline(
            sources=[
                Source(name='ERP', type='sap', refresh='real_time'),
                Source(name='HR', type='workday', refresh='hourly'),
                Source(name='Procurement', type='ariba', refresh='real_time'),
                Source(name='CRM', type='salesforce', refresh='hourly'),
                Source(name='IT', type='active_directory', refresh='real_time')
            ],
            transformations=[
                'data_cleaning', 'enrichment', 'validation'
            ],
            destinations=[
                Destination(name='warehouse', type='snowflake'),
                Destination(name='streaming', type='kafka')
            ]
        )
    
    def monitor_sod(self) -> Dict:
        # Real-time Segregation of Duties monitoring
        sod_results = {}
        
        # Rule 1: Vendor creation/approval
        sod_results['vendor'] = self.detect_sod_violations(
            rule='vendor_creation_approval',
            users=self.get_active_users(),
            transactions=self.get_vendor_transactions()
        )
        
        # Rule 2: Payment creation/processing
        sod_results['payment'] = self.detect_sod_violations(
            rule='payment_creation_processing',
            users=self.get_active_users(),
            transactions=self.get_payment_transactions()
        )
        
        return {
            'violations': sod_results,
            'risk_scores': self.calculate_sod_risk_scores(sod_results),
            'trends': self.analyze_sod_trends(sod_results)
        }
    
    def monitor_journal_entries(self) -> Dict:
        # Real-time journal entry monitoring
        return {
            'unusual': self.detect_unusual_journal_entries(
                criteria=['amount > $1M', 'after_hours', 'round_amounts']
            ),
            'sod': self.monitor_journal_entry_sod(),
            'manual': self.analyze_manual_journal_entries(),
            'topside': self.detect_topside_adjustments()
        }

# Quantum Compliance Engine
class QuantumComplianceEngine:
    def __init__(self, regulatory_universe, control_framework):
        self.regulatory_universe = regulatory_universe
        self.control_framework = control_framework
        self.mapping_engine = self.initialize_mapping_engine()
        
    def map_regulations_to_controls(self) -> Dict:
        mapping_results = {}
        
        for regulation in self.regulatory_universe.values():
            matching_controls = self.find_matching_controls(regulation)
            confidence = self.calculate_match_confidence(regulation, matching_controls)
            
            mapping_results[regulation.name] = {
                'regulation': regulation,
                'controls': matching_controls,
                'confidence': confidence,
                'gaps': self.identify_coverage_gaps(matching_controls)
            }
        
        return mapping_results
    
    def generate_compliance_reporting(self) -> Dict:
        return {
            'executive_report': {
                'summary': self.generate_executive_summary(),
                'metrics': self.calculate_key_metrics(),
                'heat_map': self.generate_risk_heat_map()
            },
            'regulatory_filings': {
                'sox_404': self.prepare_sox_404_filing(),
                'gdpr': self.prepare_gdpr_compliance_report(),
                'pci_dss': self.prepare_pci_dss_report()
            },
            'board_reporting': {
                'audit_committee': self.prepare_audit_committee_package(),
                'risk_committee': self.prepare_risk_committee_package()
            }
        }