# Identity-first security framework
class IdentityFirstSecurityFramework:
    def __init__(self):
        self.principles = {
            'never_trust_always_verify': NeverTrustAlwaysVerifyEngine(),
            'least_privilege_access': LeastPrivilegeEngine(),
            'continuous_verification': ContinuousVerificationEngine(),
            'context_aware_access': ContextAwareAccessEngine(),
            'adaptive_authentication': AdaptiveAuthenticationEngine()
        }
        self.identity_store = IdentityStore()
        self.policy_engine = PolicyEngine()
        self.risk_engine = RiskAssessmentEngine()
    
    def evaluate_access_request(self, identity, resource, context):
        """Evaluate access request using identity-first principles"""
        access_evaluation = {
            'identity_id': identity['id'],
            'resource': resource['name'],
            'request_timestamp': datetime.utcnow(),
            'verification_results': {},
            'risk_assessment': {},
            'access_decision': 'DENY',
            'adaptive_controls': []
        }
        
        # Never trust, always verify
        verification_result = self.principles['never_trust_always_verify'].verify(
            identity, context
        )
        access_evaluation['verification_results']['trust_verification'] = verification_result
        
        # Evaluate least privilege
        privilege_result = self.principles['least_privilege_access'].evaluate(
            identity, resource, context
        )
        access_evaluation['verification_results']['privilege_check'] = privilege_result
        
        # Continuous verification
        continuous_result = self.principles['continuous_verification'].verify(
            identity, context
        )
        access_evaluation['verification_results']['continuous_verification'] = continuous_result
        
        # Context-aware access evaluation
        context_result = self.principles['context_aware_access'].evaluate(
            identity, resource, context
        )
        access_evaluation['verification_results']['context_evaluation'] = context_result
        
        # Risk assessment
        risk_assessment = self.risk_engine.assess_risk(
            identity, resource, context, access_evaluation['verification_results']
        )
        access_evaluation['risk_assessment'] = risk_assessment
        
        # Adaptive authentication if needed
        if risk_assessment['risk_level'] == 'HIGH':
            adaptive_auth = self.principles['adaptive_authentication'].require_additional_auth(
                identity, risk_assessment
            )
            access_evaluation['adaptive_controls'].append(adaptive_auth)
        
        # Make access decision
        access_decision = self.make_access_decision(
            access_evaluation['verification_results'],
            risk_assessment,
            access_evaluation['adaptive_controls']
        )
        access_evaluation['access_decision'] = access_decision
        
        return access_evaluation

class ZeroTrustMaturityAssessment:
    def __init__(self):
        self.maturity_levels = {
            'traditional': 0,
            'initial': 1,
            'advanced': 2,
            'optimal': 3
        }
        self.pillars = {
            'identity': IdentityPillarAssessment(),
            'device': DevicePillarAssessment(),
            'network': NetworkPillarAssessment(),
            'application': ApplicationPillarAssessment(),
            'data': DataPillarAssessment(),
            'visibility_analytics': VisibilityAnalyticsPillarAssessment()
        }
    
    def assess_zero_trust_maturity(self, organization_data):
        """Assess organization's zero trust maturity across all pillars"""
        maturity_assessment = {
            'organization_id': organization_data['id'],
            'assessment_date': datetime.utcnow(),
            'pillar_scores': {},
            'overall_maturity': 0,
            'recommendations': [],
            'implementation_roadmap': {}
        }
        
        # Assess each pillar
        for pillar_name, pillar_assessor in self.pillars.items():
            pillar_score = pillar_assessor.assess(organization_data)
            maturity_assessment['pillar_scores'][pillar_name] = pillar_score
        
        # Calculate overall maturity
        overall_score = sum(
            score['maturity_level'] for score in maturity_assessment['pillar_scores'].values()
        ) / len(self.pillars)
        maturity_assessment['overall_maturity'] = overall_score
        
        # Generate recommendations
        recommendations = self.generate_maturity_recommendations(
            maturity_assessment['pillar_scores']
        )
        maturity_assessment['recommendations'] = recommendations
        
        # Create implementation roadmap
        roadmap = self.create_implementation_roadmap(
            maturity_assessment['pillar_scores'],
            recommendations
        )
        maturity_assessment['implementation_roadmap'] = roadmap
        
        return maturity_assessment
    
    def generate_maturity_recommendations(self, pillar_scores):
        """Generate recommendations based on pillar maturity scores"""
        recommendations = []
        
        for pillar_name, pillar_data in pillar_scores.items():
            if pillar_data['maturity_level'] < 2:  # Below advanced level
                pillar_recommendations = self.pillars[pillar_name].get_improvement_recommendations(
                    pillar_data
                )
                recommendations.extend([
                    {
                        'pillar': pillar_name,
                        'priority': rec['priority'],
                        'recommendation': rec['description'],
                        'effort': rec['implementation_effort'],
                        'impact': rec['security_impact']
                    }
                    for rec in pillar_recommendations
                ])
        
        # Sort by priority and impact
        recommendations.sort(key=lambda x: (x['priority'], x['impact']), reverse=True)
        
        return recommendations

class AdvancedIdentityVerification:
    def __init__(self):
        self.verification_methods = {
            'biometric': BiometricVerification(),
            'behavioral': BehavioralVerification(),
            'device_based': DeviceBasedVerification(),
            'location_based': LocationBasedVerification(),
            'risk_based': RiskBasedVerification()
        }
        self.ml_engine = IdentityMLEngine()
        self.fraud_detector = IdentityFraudDetector()
    
    def perform_comprehensive_verification(self, identity_claim, verification_context):
        """Perform comprehensive identity verification using multiple methods"""
        verification_result = {
            'identity_claim': identity_claim,
            'verification_timestamp': datetime.utcnow(),
            'verification_methods_used': [],
            'verification_scores': {},
            'fraud_indicators': [],
            'confidence_score': 0,
            'verification_decision': 'FAILED'
        }
        
        # Determine required verification methods based on risk
        risk_level = self.assess_initial_risk(identity_claim, verification_context)
        required_methods = self.determine_verification_methods(risk_level)
        
        # Perform each verification method
        for method_name in required_methods:
            if method_name in self.verification_methods:
                method_result = self.verification_methods[method_name].verify(
                    identity_claim,
                    verification_context
                )
                
                verification_result['verification_methods_used'].append(method_name)
                verification_result['verification_scores'][method_name] = method_result
        
        # ML-based identity verification
        ml_verification = self.ml_engine.verify_identity(
            identity_claim,
            verification_context,
            verification_result['verification_scores']
        )
        verification_result['ml_verification'] = ml_verification
        
        # Fraud detection
        fraud_analysis = self.fraud_detector.detect_identity_fraud(
            identity_claim,
            verification_context,
            verification_result['verification_scores']
        )
        verification_result['fraud_indicators'] = fraud_analysis['indicators']
        
        # Calculate overall confidence score
        confidence_score = self.calculate_confidence_score(
            verification_result['verification_scores'],
            ml_verification,
            fraud_analysis
        )
        verification_result['confidence_score'] = confidence_score
        
        # Make verification decision
        if confidence_score > 0.8 and not fraud_analysis['is_fraud']:
            verification_result['verification_decision'] = 'VERIFIED'
        elif confidence_score > 0.6:
            verification_result['verification_decision'] = 'ADDITIONAL_VERIFICATION_REQUIRED'
        else:
            verification_result['verification_decision'] = 'FAILED'
        
        return verification_result
    
    def continuous_identity_monitoring(self, verified_identity, session_context):
        """Continuously monitor identity throughout session"""
        monitoring_result = {
            'identity_id': verified_identity['id'],
            'monitoring_start': datetime.utcnow(),
            'behavioral_changes': [],
            'risk_changes': [],
            'anomalies': [],
            'trust_score_changes': [],
            'recommended_actions': []
        }
        
        # Monitor behavioral changes
        behavioral_changes = self.verification_methods['behavioral'].monitor_changes(
            verified_identity,
            session_context
        )
        monitoring_result['behavioral_changes'] = behavioral_changes
        
        # Monitor risk changes
        risk_changes = self.verification_methods['risk_based'].monitor_risk_changes(
            verified_identity,
            session_context
        )
        monitoring_result['risk_changes'] = risk_changes
        
        # Detect anomalies
        anomalies = self.ml_engine.detect_identity_anomalies(
            verified_identity,
            session_context
        )
        monitoring_result['anomalies'] = anomalies
        
        # Calculate trust score changes
        current_trust_score = self.calculate_current_trust_score(
            verified_identity,
            behavioral_changes,
            risk_changes,
            anomalies
        )
        
        trust_score_change = current_trust_score - verified_identity['initial_trust_score']
        monitoring_result['trust_score_changes'].append({
            'timestamp': datetime.utcnow(),
            'previous_score': verified_identity['initial_trust_score'],
            'current_score': current_trust_score,
            'change': trust_score_change
        })
        
        # Generate recommended actions
        if trust_score_change < -0.3:  # Significant trust decrease
            monitoring_result['recommended_actions'].append({
                'action': 'require_reauthentication',
                'reason': 'significant_trust_decrease',
                'urgency': 'high'
            })
        elif anomalies:
            monitoring_result['recommended_actions'].append({
                'action': 'additional_verification',
                'reason': 'behavioral_anomalies_detected',
                'urgency': 'medium'
            })
        
        return monitoring_result

class DynamicABACPolicyEngine:
    def __init__(self):
        self.policy_store = PolicyStore()
        self.attribute_resolver = AttributeResolver()
        self.policy_evaluator = PolicyEvaluator()
        self.decision_cache = DecisionCache()
        self.audit_logger = AccessAuditLogger()
    
    def evaluate_access_request(self, subject, resource, action, environment):
        """Evaluate access request using ABAC policies"""
        access_request = {
            'request_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'subject': subject,
            'resource': resource,
            'action': action,
            'environment': environment
        }
        
        # Check decision cache first
        cache_key = self.generate_cache_key(subject, resource, action, environment)
        cached_decision = self.decision_cache.get(cache_key)
        
        if cached_decision and not self.is_cache_expired(cached_decision):
            self.audit_logger.log_cached_decision(access_request, cached_decision)
            return cached_decision
        
        # Resolve all attributes
        resolved_attributes = {
            'subject_attributes': self.attribute_resolver.resolve_subject_attributes(subject),
            'resource_attributes': self.attribute_resolver.resolve_resource_attributes(resource),
            'action_attributes': self.attribute_resolver.resolve_action_attributes(action),
            'environment_attributes': self.attribute_resolver.resolve_environment_attributes(environment)
        }
        
        # Get applicable policies
        applicable_policies = self.policy_store.get_applicable_policies(
            resolved_attributes
        )
        
        # Evaluate policies
        policy_decisions = []
        for policy in applicable_policies:
            policy_decision = self.policy_evaluator.evaluate_policy(
                policy,
                resolved_attributes
            )
            policy_decisions.append(policy_decision)
        
        # Combine policy decisions
        final_decision = self.combine_policy_decisions(policy_decisions)
        
        # Add additional context
        access_decision = {
            'request_id': access_request['request_id'],
            'decision': final_decision['decision'],
            'confidence': final_decision['confidence'],
            'applicable_policies': [p['id'] for p in applicable_policies],
            'policy_decisions': policy_decisions,
            'resolved_attributes': resolved_attributes,
            'decision_timestamp': datetime.utcnow(),
            'obligations': final_decision.get('obligations', []),
            'advice': final_decision.get('advice', [])
        }
        
        # Cache decision
        self.decision_cache.store(cache_key, access_decision, ttl=300)  # 5 minutes
        
        # Audit log
        self.audit_logger.log_access_decision(access_request, access_decision)
        
        return access_decision
    
    def dynamic_policy_adaptation(self, access_patterns, security_events):
        """Dynamically adapt policies based on access patterns and security events"""
        adaptation_analysis = {
            'analysis_timestamp': datetime.utcnow(),
            'access_patterns_analyzed': len(access_patterns),
            'security_events_analyzed': len(security_events),
            'policy_recommendations': [],
            'risk_adjustments': [],
            'new_policies': []
        }
        
        # Analyze access patterns for policy optimization
        pattern_analysis = self.analyze_access_patterns(access_patterns)
        
        # Identify overly permissive policies
        permissive_policies = pattern_analysis['overly_permissive_policies']
        for policy in permissive_policies:
            adaptation_analysis['policy_recommendations'].append({
                'policy_id': policy['id'],
                'recommendation': 'tighten_permissions',
                'reason': 'excessive_access_granted',
                'suggested_changes': policy['suggested_restrictions']
            })
        
        # Identify overly restrictive policies
        restrictive_policies = pattern_analysis['overly_restrictive_policies']
        for policy in restrictive_policies:
            adaptation_analysis['policy_recommendations'].append({
                'policy_id': policy['id'],
                'recommendation': 'relax_permissions',
                'reason': 'legitimate_access_denied',
                'suggested_changes': policy['suggested_relaxations']
            })
        
        # Analyze security events for risk adjustments
        security_analysis = self.analyze_security_events(security_events)
        
        # Adjust risk-based policies
        for risk_adjustment in security_analysis['risk_adjustments']:
            adaptation_analysis['risk_adjustments'].append({
                'risk_factor': risk_adjustment['factor'],
                'adjustment_type': risk_adjustment['type'],
                'magnitude': risk_adjustment['magnitude'],
                'affected_policies': risk_adjustment['affected_policies']
            })
        
        # Generate new policies for emerging patterns
        new_policy_suggestions = self.generate_new_policy_suggestions(
            pattern_analysis,
            security_analysis
        )
        adaptation_analysis['new_policies'] = new_policy_suggestions
        
        return adaptation_analysis

class IntelligentRiskAssessment:
    def __init__(self):
        self.risk_factors = {
            'identity_risk': IdentityRiskCalculator(),
            'device_risk': DeviceRiskCalculator(),
            'location_risk': LocationRiskCalculator(),
            'behavioral_risk': BehavioralRiskCalculator(),
            'contextual_risk': ContextualRiskCalculator()
        }
        self.ml_risk_model = MLRiskModel()
        self.threat_intelligence = ThreatIntelligenceService()
    
    def calculate_comprehensive_risk_score(self, access_context):
        """Calculate comprehensive risk score for access decision"""
        risk_assessment = {
            'assessment_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'access_context': access_context,
            'individual_risk_scores': {},
            'threat_intelligence_factors': {},
            'ml_risk_prediction': {},
            'overall_risk_score': 0,
            'risk_level': 'UNKNOWN',
            'contributing_factors': []
        }
        
        # Calculate individual risk factors
        for factor_name, calculator in self.risk_factors.items():
            risk_score = calculator.calculate_risk(access_context)
            risk_assessment['individual_risk_scores'][factor_name] = risk_score
            
            if risk_score['score'] > 0.7:  # High risk factor
                risk_assessment['contributing_factors'].append({
                    'factor': factor_name,
                    'score': risk_score['score'],
                    'reasons': risk_score['reasons']
                })
        
        # Incorporate threat intelligence
        threat_factors = self.threat_intelligence.get_relevant_threats(
            access_context
        )
        risk_assessment['threat_intelligence_factors'] = threat_factors
        
        # ML-based risk prediction
        ml_prediction = self.ml_risk_model.predict_risk(
            access_context,
            risk_assessment['individual_risk_scores'],
            threat_factors
        )
        risk_assessment['ml_risk_prediction'] = ml_prediction
        
        # Calculate overall risk score
        overall_risk = self.calculate_weighted_risk_score(
            risk_assessment['individual_risk_scores'],
            threat_factors,
            ml_prediction
        )
        risk_assessment['overall_risk_score'] = overall_risk
        
        # Determine risk level
        if overall_risk < 0.3:
            risk_assessment['risk_level'] = 'LOW'
        elif overall_risk < 0.6:
            risk_assessment['risk_level'] = 'MEDIUM'
        elif overall_risk < 0.8:
            risk_assessment['risk_level'] = 'HIGH'
        else:
            risk_assessment['risk_level'] = 'CRITICAL'
        
        return risk_assessment
    
    def adaptive_risk_thresholds(self, historical_data, current_threat_landscape):
        """Dynamically adjust risk thresholds based on historical data and threats"""
        threshold_analysis = {
            'analysis_timestamp': datetime.utcnow(),
            'historical_period': historical_data['period'],
            'current_thresholds': self.get_current_thresholds(),
            'recommended_thresholds': {},
            'adjustment_reasons': []
        }
        
        # Analyze historical false positives/negatives
        historical_analysis = self.analyze_historical_decisions(historical_data)
        
        # Adjust thresholds based on false positive rate
        if historical_analysis['false_positive_rate'] > 0.1:  # Too many false positives
            threshold_analysis['recommended_thresholds']['increase_threshold'] = {
                'current': threshold_analysis['current_thresholds']['medium_risk'],
                'recommended': threshold_analysis['current_thresholds']['medium_risk'] + 0.1,
                'reason': 'reduce_false_positives'
            }
            threshold_analysis['adjustment_reasons'].append('high_false_positive_rate')
        
        # Adjust thresholds based on false negative rate
        if historical_analysis['false_negative_rate'] > 0.05:  # Too many false negatives
            threshold_analysis['recommended_thresholds']['decrease_threshold'] = {
                'current': threshold_analysis['current_thresholds']['high_risk'],
                'recommended': threshold_analysis['current_thresholds']['high_risk'] - 0.1,
                'reason': 'reduce_false_negatives'
            }
            threshold_analysis['adjustment_reasons'].append('high_false_negative_rate')
        
        # Adjust based on current threat landscape
        threat_adjustment = self.calculate_threat_based_adjustment(current_threat_landscape)
        if threat_adjustment['adjustment_needed']:
            threshold_analysis['recommended_thresholds']['threat_adjustment'] = threat_adjustment
            threshold_analysis['adjustment_reasons'].append('threat_landscape_change')
        
        return threshold_analysis

class IdentityLifecycleManager:
    def __init__(self):
        self.provisioning_engine = ProvisioningEngine()
        self.deprovisioning_engine = DeprovisioningEngine()
        self.access_reviewer = AccessReviewer()
        self.compliance_monitor = ComplianceMonitor()
        self.workflow_engine = WorkflowEngine()
    
    def manage_identity_lifecycle(self, identity_event):
        """Manage complete identity lifecycle based on events"""
        lifecycle_management = {
            'event_id': identity_event['id'],
            'event_type': identity_event['type'],
            'identity_id': identity_event['identity_id'],
            'timestamp': datetime.utcnow(),
            'actions_taken': [],
            'compliance_checks': [],
            'workflow_status': 'INITIATED'
        }
        
        if identity_event['type'] == 'JOINER':
            # New employee/user joining
            joiner_actions = self.handle_joiner_event(identity_event)
            lifecycle_management['actions_taken'].extend(joiner_actions)
            
        elif identity_event['type'] == 'MOVER':
            # Employee/user changing roles
            mover_actions = self.handle_mover_event(identity_event)
            lifecycle_management['actions_taken'].extend(mover_actions)
            
        elif identity_event['type'] == 'LEAVER':
            # Employee/user leaving
            leaver_actions = self.handle_leaver_event(identity_event)
            lifecycle_management['actions_taken'].extend(leaver_actions)
            
        elif identity_event['type'] == 'ACCESS_REVIEW':
            # Periodic access review
            review_actions = self.handle_access_review_event(identity_event)
            lifecycle_management['actions_taken'].extend(review_actions)
        
        # Perform compliance checks
        compliance_results = self.compliance_monitor.check_compliance(
            identity_event,
            lifecycle_management['actions_taken']
        )
        lifecycle_management['compliance_checks'] = compliance_results
        
        # Update workflow status
        lifecycle_management['workflow_status'] = 'COMPLETED'
        
        return lifecycle_management
    
    def handle_joiner_event(self, joiner_event):
        """Handle new joiner identity provisioning"""
        joiner_actions = []
        
        # Create identity
        identity_creation = self.provisioning_engine.create_identity(
            joiner_event['identity_data']
        )
        joiner_actions.append({
            'action': 'create_identity',
            'result': identity_creation,
            'timestamp': datetime.utcnow()
        })
        
        # Provision role-based access
        role_provisioning = self.provisioning_engine.provision_role_access(
            joiner_event['identity_id'],
            joiner_event['role_assignments']
        )
        joiner_actions.append({
            'action': 'provision_role_access',
            'result': role_provisioning,
            'timestamp': datetime.utcnow()
        })
        
        # Set up authentication methods
        auth_setup = self.provisioning_engine.setup_authentication(
            joiner_event['identity_id'],
            joiner_event['auth_requirements']
        )
        joiner_actions.append({
            'action': 'setup_authentication',
            'result': auth_setup,
            'timestamp': datetime.utcnow()
        })
        
        # Schedule access review
        review_scheduling = self.access_reviewer.schedule_access_review(
            joiner_event['identity_id'],
            joiner_event['review_schedule']
        )
        joiner_actions.append({
            'action': 'schedule_access_review',
            'result': review_scheduling,
            'timestamp': datetime.utcnow()
        })
        
        return joiner_actions
    
    def intelligent_access_certification(self, certification_campaign):
        """Perform intelligent access certification using ML"""
        certification_result = {
            'campaign_id': certification_campaign['id'],
            'start_timestamp': datetime.utcnow(),
            'total_access_items': len(certification_campaign['access_items']),
            'auto_certified': [],
            'requires_review': [],
            'high_risk_items': [],
            'ml_recommendations': []
        }
        
        for access_item in certification_campaign['access_items']:
            # ML-based risk assessment
            risk_assessment = self.assess_access_risk(access_item)
            
            # ML-based certification recommendation
            ml_recommendation = self.generate_certification_recommendation(
                access_item,
                risk_assessment
            )
            
            certification_result['ml_recommendations'].append({
                'access_item_id': access_item['id'],
                'recommendation': ml_recommendation['action'],
                'confidence': ml_recommendation['confidence'],
                'reasoning': ml_recommendation['reasoning']
            })
            
            # Categorize based on ML recommendation
            if ml_recommendation['action'] == 'AUTO_CERTIFY' and ml_recommendation['confidence'] > 0.9:
                certification_result['auto_certified'].append(access_item['id'])
            elif risk_assessment['risk_level'] == 'HIGH':
                certification_result['high_risk_items'].append(access_item['id'])
            else:
                certification_result['requires_review'].append(access_item['id'])
        
        # Generate certification summary
        certification_result['summary'] = {
            'auto_certification_rate': len(certification_result['auto_certified']) / certification_result['total_access_items'],
            'manual_review_rate': len(certification_result['requires_review']) / certification_result['total_access_items'],
            'high_risk_rate': len(certification_result['high_risk_items']) / certification_result['total_access_items']
        }
        
        return certification_result

class PrivacyPreservingIdentityManager:
    def __init__(self):
        self.consent_manager = ConsentManager()
        self.data_minimizer = DataMinimizer()
        self.anonymization_engine = AnonymizationEngine()
        self.retention_manager = RetentionManager()
        self.audit_logger = PrivacyAuditLogger()
    
    def process_identity_data_with_privacy(self, identity_data, processing_purpose):
        """Process identity data with privacy preservation"""
        privacy_processing = {
            'processing_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'identity_id': identity_data['id'],
            'processing_purpose': processing_purpose,
            'consent_status': {},
            'data_minimization': {},
            'anonymization': {},
            'retention_policy': {},
            'processing_allowed': False
        }
        
        # Check consent
        consent_status = self.consent_manager.check_consent(
            identity_data['id'],
            processing_purpose
        )
        privacy_processing['consent_status'] = consent_status
        
        if consent_status['has_valid_consent']:
            # Apply data minimization
            minimized_data = self.data_minimizer.minimize_data(
                identity_data,
                processing_purpose
            )
            privacy_processing['data_minimization'] = {
                'original_fields': len(identity_data.keys()),
                'minimized_fields': len(minimized_data.keys()),
                'removed_fields': list(set(identity_data.keys()) - set(minimized_data.keys()))
            }
            
            # Apply anonymization if required
            if processing_purpose['requires_anonymization']:
                anonymized_data = self.anonymization_engine.anonymize_data(
                    minimized_data,
                    processing_purpose['anonymization_level']
                )
                privacy_processing['anonymization'] = {
                    'anonymization_applied': True,
                    'anonymization_level': processing_purpose['anonymization_level'],
                    'k_anonymity': anonymized_data['k_anonymity_level']
                }
                processed_data = anonymized_data['data']
            else:
                processed_data = minimized_data
            
            # Set retention policy
            retention_policy = self.retention_manager.get_retention_policy(
                processing_purpose
            )
            privacy_processing['retention_policy'] = retention_policy
            
            # Schedule data deletion
            self.retention_manager.schedule_data_deletion(
                privacy_processing['processing_id'],
                retention_policy['retention_period']
            )
            
            privacy_processing['processing_allowed'] = True
            privacy_processing['processed_data'] = processed_data
            
            # Audit log
            self.audit_logger.log_privacy_processing(privacy_processing)
        
        return privacy_processing
    
    def handle_data_subject_rights(self, identity_id, rights_request):
        """Handle GDPR data subject rights requests"""
        rights_response = {
            'request_id': rights_request['id'],
            'identity_id': identity_id,
            'request_type': rights_request['type'],
            'timestamp': datetime.utcnow(),
            'processing_status': 'IN_PROGRESS',
            'response_data': {},
            'actions_taken': []
        }
        
        if rights_request['type'] == 'ACCESS':
            # Right to access
            access_data = self.provide_data_access(identity_id)
            rights_response['response_data'] = access_data
            rights_response['actions_taken'].append('data_access_provided')
            
        elif rights_request['type'] == 'RECTIFICATION':
            # Right to rectification
            rectification_result = self.rectify_data(
                identity_id,
                rights_request['rectification_data']
            )
            rights_response['response_data'] = rectification_result
            rights_response['actions_taken'].append('data_rectified')
            
        elif rights_request['type'] == 'ERASURE':
            # Right to erasure (right to be forgotten)
            erasure_result = self.erase_data(identity_id)
            rights_response['response_data'] = erasure_result
            rights_response['actions_taken'].append('data_erased')
            
        elif rights_request['type'] == 'PORTABILITY':
            # Right to data portability
            portable_data = self.export_portable_data(identity_id)
            rights_response['response_data'] = portable_data
            rights_response['actions_taken'].append('portable_data_provided')
            
        elif rights_request['type'] == 'RESTRICTION':
            # Right to restriction of processing
            restriction_result = self.restrict_processing(identity_id)
            rights_response['response_data'] = restriction_result
            rights_response['actions_taken'].append('processing_restricted')
        
        rights_response['processing_status'] = 'COMPLETED'
        
        # Audit log
        self.audit_logger.log_rights_request_handling(rights_response)
        
        return rights_response

class QuantumSafeIdentitySystem:
    def __init__(self):
        self.quantum_crypto = PostQuantumCryptography()
        self.quantum_key_manager = QuantumKeyManager()
        self.quantum_signature = QuantumDigitalSignature()
        self.classical_fallback = ClassicalIdentitySystem()
    
    def create_quantum_safe_identity(self, identity_data):
        """Create quantum-safe digital identity"""
        quantum_identity = {
            'identity_id': str(uuid.uuid4()),
            'creation_timestamp': datetime.utcnow(),
            'quantum_safe': True,
            'cryptographic_suite': 'POST_QUANTUM',
            'key_pairs': {},
            'certificates': {},
            'signatures': {}
        }
        
        # Generate post-quantum key pairs
        signing_keypair = self.quantum_crypto.generate_signing_keypair('CRYSTALS-Dilithium-3')
        encryption_keypair = self.quantum_crypto.generate_encryption_keypair('CRYSTALS-Kyber-768')
        
        quantum_identity['key_pairs'] = {
            'signing': {
                'algorithm': 'CRYSTALS-Dilithium-3',
                'public_key': signing_keypair['public_key'],
                'private_key': signing_keypair['private_key'],
                'key_size': len(signing_keypair['public_key'])
            },
            'encryption': {
                'algorithm': 'CRYSTALS-Kyber-768',
                'public_key': encryption_keypair['public_key'],
                'private_key': encryption_keypair['private_key'],
                'key_size': len(encryption_keypair['public_key'])
            }
        }
        
        # Create quantum-safe certificates
        identity_certificate = self.create_quantum_safe_certificate(
            identity_data,
            quantum_identity['key_pairs']['signing']['public_key']
        )
        quantum_identity['certificates']['identity'] = identity_certificate
        
        # Sign identity data with post-quantum signature
        identity_signature = self.quantum_signature.sign_data(
            identity_data,
            quantum_identity['key_pairs']['signing']['private_key']
        )
        quantum_identity['signatures']['identity_data'] = identity_signature
        
        return quantum_identity
    
    def quantum_safe_authentication(self, identity_claim, quantum_proof):
        """Perform quantum-safe authentication"""
        auth_result = {
            'authentication_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'identity_claim': identity_claim,
            'quantum_verification': {},
            'authentication_status': 'FAILED'
        }
        
        # Verify quantum-safe signature
        signature_verification = self.quantum_signature.verify_signature(
            identity_claim['identity_data'],
            quantum_proof['signature'],
            identity_claim['public_key']
        )
        auth_result['quantum_verification']['signature'] = signature_verification
        
        # Verify quantum-safe certificate
        certificate_verification = self.verify_quantum_safe_certificate(
            identity_claim['certificate']
        )
        auth_result['quantum_verification']['certificate'] = certificate_verification
        
        # Perform quantum key exchange for session establishment
        if signature_verification['valid'] and certificate_verification['valid']:
            session_key = self.quantum_crypto.establish_session_key(
                identity_claim['public_key']
            )
            auth_result['session_key'] = session_key
            auth_result['authentication_status'] = 'SUCCESS'
        
        return auth_result