# AI-powered data collection framework
class AIDataCollector:
    def __init__(self):
        self.collectors = {
            'web': WebExperienceCollector(),
            'mobile': MobileAppCollector(),
            'api': APIPerformanceCollector(),
            'network': NetworkTelemetryCollector(),
            'security': SecurityEventCollector()
        }
        self.ai_processor = AIDataProcessor()
    
    def collect_comprehensive_telemetry(self, user_session):
        """Collect comprehensive telemetry data for AI analysis"""
        telemetry_data = {}
        
        for source, collector in self.collectors.items():
            raw_data = collector.collect(user_session)
            processed_data = self.ai_processor.preprocess(raw_data, source)
            telemetry_data[source] = processed_data
        
        # Apply AI-powered data fusion
        unified_telemetry = self.ai_processor.fuse_data_sources(telemetry_data)
        
        return {
            'session_id': user_session.id,
            'timestamp': datetime.utcnow(),
            'raw_data': telemetry_data,
            'unified_data': unified_telemetry,
            'ai_insights': self.ai_processor.generate_insights(unified_telemetry)
        }

# AI analytics engine for digital experience monitoring
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.cluster import DBSCAN
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout

class AIExperienceAnalytics:
    def __init__(self):
        self.anomaly_detector = IsolationForest(contamination=0.1)
        self.behavior_clusterer = DBSCAN(eps=0.5, min_samples=5)
        self.sequence_analyzer = self.build_lstm_model()
        self.threat_classifier = ThreatClassificationModel()
    
    def build_lstm_model(self):
        """Build LSTM model for sequence analysis"""
        model = Sequential([
            LSTM(128, return_sequences=True, input_shape=(100, 50)),
            Dropout(0.2),
            LSTM(64, return_sequences=False),
            Dropout(0.2),
            Dense(32, activation='relu'),
            Dense(1, activation='sigmoid')
        ])
        
        model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
        return model
    
    def analyze_user_journey(self, user_telemetry):
        """Analyze complete user journey using AI"""
        journey_analysis = {
            'user_id': user_telemetry['user_id'],
            'session_data': user_telemetry['sessions'],
            'behavioral_patterns': {},
            'anomalies': [],
            'security_insights': {},
            'experience_metrics': {}
        }
        
        # Behavioral pattern analysis
        behavior_features = self.extract_behavioral_features(user_telemetry)
        journey_analysis['behavioral_patterns'] = self.analyze_behavior_patterns(behavior_features)
        
        # Anomaly detection
        anomalies = self.detect_behavioral_anomalies(behavior_features)
        journey_analysis['anomalies'] = anomalies
        
        # Security threat analysis
        security_features = self.extract_security_features(user_telemetry)
        journey_analysis['security_insights'] = self.analyze_security_threats(security_features)
        
        # Experience quality assessment
        experience_features = self.extract_experience_features(user_telemetry)
        journey_analysis['experience_metrics'] = self.assess_experience_quality(experience_features)
        
        return journey_analysis
    
    def detect_behavioral_anomalies(self, behavior_features):
        """Detect anomalies in user behavior using AI"""
        # Prepare feature matrix
        feature_matrix = np.array([list(features.values()) for features in behavior_features])
        
        # Detect anomalies
        anomaly_scores = self.anomaly_detector.decision_function(feature_matrix)
        anomaly_labels = self.anomaly_detector.predict(feature_matrix)
        
        anomalies = []
        for i, (score, label) in enumerate(zip(anomaly_scores, anomaly_labels)):
            if label == -1:  # Anomaly detected
                anomalies.append({
                    'timestamp': behavior_features[i]['timestamp'],
                    'anomaly_score': score,
                    'features': behavior_features[i],
                    'severity': self.calculate_anomaly_severity(score),
                    'potential_threats': self.identify_potential_threats(behavior_features[i])
                })
        
        return anomalies

class BehavioralBiometricsEngine:
    def __init__(self):
        self.keystroke_analyzer = KeystrokeDynamicsAnalyzer()
        self.mouse_analyzer = MouseDynamicsAnalyzer()
        self.touch_analyzer = TouchDynamicsAnalyzer()
        self.gait_analyzer = GaitAnalysisEngine()
        self.behavioral_model = BehavioralAuthenticationModel()
    
    def create_behavioral_profile(self, user_id, interaction_data):
        """Create comprehensive behavioral profile"""
        profile = {
            'user_id': user_id,
            'keystroke_patterns': {},
            'mouse_patterns': {},
            'touch_patterns': {},
            'navigation_patterns': {},
            'temporal_patterns': {},
            'device_interaction_patterns': {}
        }
        
        # Analyze keystroke dynamics
        if 'keystrokes' in interaction_data:
            profile['keystroke_patterns'] = self.keystroke_analyzer.analyze(
                interaction_data['keystrokes']
            )
        
        # Analyze mouse dynamics
        if 'mouse_movements' in interaction_data:
            profile['mouse_patterns'] = self.mouse_analyzer.analyze(
                interaction_data['mouse_movements']
            )
        
        # Analyze touch patterns (mobile)
        if 'touch_events' in interaction_data:
            profile['touch_patterns'] = self.touch_analyzer.analyze(
                interaction_data['touch_events']
            )
        
        # Analyze navigation patterns
        profile['navigation_patterns'] = self.analyze_navigation_behavior(
            interaction_data['page_visits']
        )
        
        # Analyze temporal patterns
        profile['temporal_patterns'] = self.analyze_temporal_behavior(
            interaction_data['session_times']
        )
        
        return profile
    
    def continuous_authentication(self, user_id, current_behavior):
        """Perform continuous authentication based on behavior"""
        stored_profile = self.get_user_profile(user_id)
        
        # Calculate behavioral similarity scores
        similarity_scores = {
            'keystroke': self.keystroke_analyzer.calculate_similarity(
                stored_profile['keystroke_patterns'],
                current_behavior.get('keystrokes', {})
            ),
            'mouse': self.mouse_analyzer.calculate_similarity(
                stored_profile['mouse_patterns'],
                current_behavior.get('mouse_movements', {})
            ),
            'navigation': self.calculate_navigation_similarity(
                stored_profile['navigation_patterns'],
                current_behavior.get('navigation', {})
            )
        }
        
        # Calculate overall authentication confidence
        confidence_score = self.behavioral_model.calculate_confidence(similarity_scores)
        
        authentication_result = {
            'user_id': user_id,
            'confidence_score': confidence_score,
            'similarity_scores': similarity_scores,
            'authentication_status': 'AUTHENTICATED' if confidence_score > 0.8 else 'SUSPICIOUS',
            'risk_level': self.calculate_risk_level(confidence_score),
            'recommended_action': self.recommend_action(confidence_score)
        }
        
        return authentication_result

class AIThreatCorrelationEngine:
    def __init__(self):
        self.threat_models = {
            'account_takeover': AccountTakeoverModel(),
            'insider_threat': InsiderThreatModel(),
            'bot_detection': BotDetectionModel(),
            'fraud_detection': FraudDetectionModel(),
            'data_exfiltration': DataExfiltrationModel()
        }
        self.correlation_engine = ThreatCorrelationEngine()
        self.risk_calculator = RiskCalculationEngine()
    
    def analyze_security_threats(self, user_telemetry):
        """Comprehensive security threat analysis"""
        threat_analysis = {
            'user_id': user_telemetry['user_id'],
            'analysis_timestamp': datetime.utcnow(),
            'threat_scores': {},
            'correlated_threats': [],
            'risk_assessment': {},
            'recommended_actions': []
        }
        
        # Run individual threat detection models
        for threat_type, model in self.threat_models.items():
            threat_score = model.analyze(user_telemetry)
            threat_analysis['threat_scores'][threat_type] = threat_score
        
        # Correlate threats across different models
        correlated_threats = self.correlation_engine.correlate_threats(
            threat_analysis['threat_scores'],
            user_telemetry
        )
        threat_analysis['correlated_threats'] = correlated_threats
        
        # Calculate overall risk assessment
        risk_assessment = self.risk_calculator.calculate_risk(
            threat_analysis['threat_scores'],
            correlated_threats,
            user_telemetry['user_context']
        )
        threat_analysis['risk_assessment'] = risk_assessment
        
        # Generate recommended actions
        recommended_actions = self.generate_threat_response_actions(
            threat_analysis['threat_scores'],
            risk_assessment
        )
        threat_analysis['recommended_actions'] = recommended_actions
        
        return threat_analysis
    
    def detect_account_takeover(self, user_session_data):
        """Detect potential account takeover attempts"""
        ato_indicators = {
            'location_anomaly': self.detect_location_anomaly(user_session_data),
            'device_anomaly': self.detect_device_anomaly(user_session_data),
            'behavior_anomaly': self.detect_behavior_anomaly(user_session_data),
            'access_pattern_anomaly': self.detect_access_pattern_anomaly(user_session_data),
            'velocity_anomaly': self.detect_velocity_anomaly(user_session_data)
        }
        
        # Calculate ATO risk score
        ato_score = self.calculate_ato_score(ato_indicators)
        
        return {
            'ato_risk_score': ato_score,
            'indicators': ato_indicators,
            'confidence_level': self.calculate_confidence_level(ato_indicators),
            'recommended_response': self.recommend_ato_response(ato_score)
        }

class AIPerformanceOptimizer:
    def __init__(self):
        self.performance_predictor = PerformancePredictionModel()
        self.resource_optimizer = ResourceOptimizationEngine()
        self.content_optimizer = ContentOptimizationEngine()
        self.network_optimizer = NetworkOptimizationEngine()
    
    def optimize_user_experience(self, user_context, performance_data):
        """Optimize user experience using AI predictions"""
        optimization_strategy = {
            'user_id': user_context['user_id'],
            'current_performance': performance_data,
            'predicted_issues': [],
            'optimization_actions': [],
            'expected_improvements': {}
        }
        
        # Predict potential performance issues
        predicted_issues = self.performance_predictor.predict_issues(
            user_context,
            performance_data
        )
        optimization_strategy['predicted_issues'] = predicted_issues
        
        # Generate optimization actions
        for issue in predicted_issues:
            if issue['type'] == 'slow_loading':
                actions = self.optimize_loading_performance(user_context, issue)
            elif issue['type'] == 'high_latency':
                actions = self.optimize_network_performance(user_context, issue)
            elif issue['type'] == 'resource_contention':
                actions = self.optimize_resource_allocation(user_context, issue)
            else:
                actions = self.generate_generic_optimization(user_context, issue)
            
            optimization_strategy['optimization_actions'].extend(actions)
        
        # Calculate expected improvements
        expected_improvements = self.calculate_expected_improvements(
            optimization_strategy['optimization_actions']
        )
        optimization_strategy['expected_improvements'] = expected_improvements
        
        return optimization_strategy
    
    def adaptive_content_delivery(self, user_profile, content_request):
        """Adapt content delivery based on user profile and context"""
        delivery_strategy = {
            'content_id': content_request['content_id'],
            'user_profile': user_profile,
            'delivery_method': 'standard',
            'optimizations': []
        }
        
        # Analyze user context
        device_capabilities = user_profile['device_info']
        network_conditions = user_profile['network_info']
        user_preferences = user_profile['preferences']
        
        # Optimize based on device capabilities
        if device_capabilities['screen_size'] == 'mobile':
            delivery_strategy['optimizations'].append({
                'type': 'responsive_images',
                'action': 'serve_mobile_optimized_images'
            })
        
        # Optimize based on network conditions
        if network_conditions['bandwidth'] < 1000000:  # Less than 1 Mbps
            delivery_strategy['optimizations'].append({
                'type': 'compression',
                'action': 'enable_aggressive_compression'
            })
            delivery_strategy['delivery_method'] = 'progressive'
        
        # Optimize based on user behavior patterns
        if user_profile['behavior_patterns']['impatient_user']:
            delivery_strategy['optimizations'].append({
                'type': 'preloading',
                'action': 'preload_likely_next_content'
            })
        
        return delivery_strategy

class PredictiveExperienceAnalytics:
    def __init__(self):
        self.churn_predictor = ChurnPredictionModel()
        self.satisfaction_predictor = SatisfactionPredictionModel()
        self.conversion_predictor = ConversionPredictionModel()
        self.engagement_predictor = EngagementPredictionModel()
    
    def predict_user_outcomes(self, user_journey_data):
        """Predict various user outcomes based on journey data"""
        predictions = {
            'user_id': user_journey_data['user_id'],
            'prediction_timestamp': datetime.utcnow(),
            'churn_risk': {},
            'satisfaction_score': {},
            'conversion_probability': {},
            'engagement_level': {},
            'recommended_interventions': []
        }
        
        # Predict churn risk
        churn_features = self.extract_churn_features(user_journey_data)
        churn_prediction = self.churn_predictor.predict(churn_features)
        predictions['churn_risk'] = {
            'probability': churn_prediction['probability'],
            'risk_level': churn_prediction['risk_level'],
            'key_factors': churn_prediction['contributing_factors']
        }
        
        # Predict satisfaction score
        satisfaction_features = self.extract_satisfaction_features(user_journey_data)
        satisfaction_prediction = self.satisfaction_predictor.predict(satisfaction_features)
        predictions['satisfaction_score'] = {
            'predicted_score': satisfaction_prediction['score'],
            'confidence_interval': satisfaction_prediction['confidence'],
            'improvement_opportunities': satisfaction_prediction['improvements']
        }
        
        # Predict conversion probability
        conversion_features = self.extract_conversion_features(user_journey_data)
        conversion_prediction = self.conversion_predictor.predict(conversion_features)
        predictions['conversion_probability'] = {
            'probability': conversion_prediction['probability'],
            'optimal_timing': conversion_prediction['timing'],
            'conversion_barriers': conversion_prediction['barriers']
        }
        
        # Generate intervention recommendations
        interventions = self.generate_intervention_recommendations(predictions)
        predictions['recommended_interventions'] = interventions
        
        return predictions
    
    def real_time_experience_scoring(self, current_session_data):
        """Calculate real-time experience score"""
        experience_metrics = {
            'performance_score': self.calculate_performance_score(current_session_data),
            'usability_score': self.calculate_usability_score(current_session_data),
            'content_relevance_score': self.calculate_content_relevance(current_session_data),
            'security_comfort_score': self.calculate_security_comfort(current_session_data),
            'overall_satisfaction': 0
        }
        
        # Calculate weighted overall satisfaction
        weights = {
            'performance_score': 0.3,
            'usability_score': 0.25,
            'content_relevance_score': 0.25,
            'security_comfort_score': 0.2
        }
        
        overall_satisfaction = sum(
            experience_metrics[metric] * weight
            for metric, weight in weights.items()
        )
        experience_metrics['overall_satisfaction'] = overall_satisfaction
        
        return experience_metrics

class AIFraudDetectionSystem:
    def __init__(self):
        self.transaction_analyzer = TransactionAnalyzer()
        self.behavior_analyzer = BehaviorAnalyzer()
        self.device_fingerprinter = DeviceFingerprintAnalyzer()
        self.network_analyzer = NetworkAnalyzer()
        self.fraud_model = EnsembleFraudModel()
    
    def detect_fraudulent_activity(self, transaction_data, user_context):
        """Comprehensive fraud detection using multiple AI models"""
        fraud_analysis = {
            'transaction_id': transaction_data['transaction_id'],
            'user_id': transaction_data['user_id'],
            'analysis_timestamp': datetime.utcnow(),
            'fraud_indicators': {},
            'risk_score': 0,
            'fraud_probability': 0,
            'recommended_action': 'ALLOW'
        }
        
        # Analyze transaction patterns
        transaction_indicators = self.transaction_analyzer.analyze(
            transaction_data,
            user_context['transaction_history']
        )
        fraud_analysis['fraud_indicators']['transaction'] = transaction_indicators
        
        # Analyze behavioral patterns
        behavior_indicators = self.behavior_analyzer.analyze(
            user_context['current_behavior'],
            user_context['behavioral_baseline']
        )
        fraud_analysis['fraud_indicators']['behavior'] = behavior_indicators
        
        # Analyze device fingerprint
        device_indicators = self.device_fingerprinter.analyze(
            user_context['device_info'],
            user_context['known_devices']
        )
        fraud_analysis['fraud_indicators']['device'] = device_indicators
        
        # Analyze network characteristics
        network_indicators = self.network_analyzer.analyze(
            user_context['network_info'],
            user_context['network_history']
        )
        fraud_analysis['fraud_indicators']['network'] = network_indicators
        
        # Calculate overall fraud probability
        fraud_probability = self.fraud_model.predict_fraud_probability(
            fraud_analysis['fraud_indicators']
        )
        fraud_analysis['fraud_probability'] = fraud_probability
        
        # Calculate risk score and recommended action
        risk_score = self.calculate_risk_score(fraud_probability, transaction_data)
        fraud_analysis['risk_score'] = risk_score
        fraud_analysis['recommended_action'] = self.determine_action(risk_score)
        
        return fraud_analysis
    
    def adaptive_fraud_thresholds(self, user_profile, transaction_context):
        """Dynamically adjust fraud detection thresholds"""
        base_threshold = 0.5
        
        # Adjust based on user risk profile
        user_risk_adjustment = self.calculate_user_risk_adjustment(user_profile)
        
        # Adjust based on transaction context
        context_adjustment = self.calculate_context_adjustment(transaction_context)
        
        # Adjust based on current threat landscape
        threat_adjustment = self.calculate_threat_landscape_adjustment()
        
        adaptive_threshold = base_threshold + user_risk_adjustment + context_adjustment + threat_adjustment
        
        # Ensure threshold stays within reasonable bounds
        adaptive_threshold = max(0.1, min(0.9, adaptive_threshold))
        
        return {
            'threshold': adaptive_threshold,
            'base_threshold': base_threshold,
            'adjustments': {
                'user_risk': user_risk_adjustment,
                'context': context_adjustment,
                'threat_landscape': threat_adjustment
            }
        }

class InsiderThreatDetectionSystem:
    def __init__(self):
        self.baseline_analyzer = BaselineBehaviorAnalyzer()
        self.anomaly_detector = BehavioralAnomalyDetector()
        self.risk_assessor = InsiderRiskAssessor()
        self.pattern_matcher = ThreatPatternMatcher()
    
    def monitor_insider_threats(self, employee_data, access_logs, system_interactions):
        """Monitor for potential insider threat indicators"""
        threat_assessment = {
            'employee_id': employee_data['employee_id'],
            'assessment_period': {
                'start': datetime.utcnow() - timedelta(days=30),
                'end': datetime.utcnow()
            },
            'behavioral_changes': [],
            'access_anomalies': [],
            'risk_indicators': [],
            'overall_risk_score': 0,
            'threat_level': 'LOW'
        }
        
        # Analyze behavioral changes
        current_behavior = self.extract_behavioral_features(
            access_logs,
            system_interactions
        )
        baseline_behavior = self.baseline_analyzer.get_baseline(employee_data['employee_id'])
        
        behavioral_changes = self.anomaly_detector.detect_changes(
            baseline_behavior,
            current_behavior
        )
        threat_assessment['behavioral_changes'] = behavioral_changes
        
        # Analyze access patterns
        access_anomalies = self.analyze_access_anomalies(
            access_logs,
            employee_data['role_permissions']
        )
        threat_assessment['access_anomalies'] = access_anomalies
        
        # Identify risk indicators
        risk_indicators = self.identify_risk_indicators(
            employee_data,
            behavioral_changes,
            access_anomalies
        )
        threat_assessment['risk_indicators'] = risk_indicators
        
        # Calculate overall risk score
        risk_score = self.risk_assessor.calculate_risk_score(
            behavioral_changes,
            access_anomalies,
            risk_indicators
        )
        threat_assessment['overall_risk_score'] = risk_score
        threat_assessment['threat_level'] = self.determine_threat_level(risk_score)
        
        return threat_assessment
    
    def detect_data_exfiltration_patterns(self, user_activity):
        """Detect patterns indicative of data exfiltration"""
        exfiltration_indicators = {
            'unusual_data_access': self.detect_unusual_data_access(user_activity),
            'large_downloads': self.detect_large_downloads(user_activity),
            'off_hours_activity': self.detect_off_hours_activity(user_activity),
            'external_communications': self.detect_external_communications(user_activity),
            'removable_media_usage': self.detect_removable_media_usage(user_activity)
        }
        
        # Calculate exfiltration risk score
        exfiltration_score = self.calculate_exfiltration_score(exfiltration_indicators)
        
        return {
            'indicators': exfiltration_indicators,
            'exfiltration_score': exfiltration_score,
            'risk_level': self.determine_exfiltration_risk_level(exfiltration_score),
            'recommended_actions': self.recommend_exfiltration_response(exfiltration_score)
        }

class AIDEMPlatform:
    def __init__(self):
        self.services = {
            'data_ingestion': DataIngestionService(),
            'ai_analytics': AIAnalyticsService(),
            'threat_detection': ThreatDetectionService(),
            'experience_optimization': ExperienceOptimizationService(),
            'alerting': AlertingService(),
            'reporting': ReportingService()
        }
        self.message_broker = MessageBroker()
        self.data_lake = DataLakeStorage()
        self.ml_pipeline = MLPipelineOrchestrator()
    
    def process_user_telemetry(self, telemetry_data):
        """Process user telemetry through AI-DEM pipeline"""
        processing_pipeline = {
            'ingestion_id': str(uuid.uuid4()),
            'telemetry_data': telemetry_data,
            'processing_stages': [],
            'results': {}
        }
        
        # Stage 1: Data Ingestion and Validation
        ingestion_result = self.services['data_ingestion'].process(telemetry_data)
        processing_pipeline['processing_stages'].append('data_ingestion')
        processing_pipeline['results']['ingestion'] = ingestion_result
        
        # Stage 2: AI Analytics Processing
        analytics_result = self.services['ai_analytics'].analyze(ingestion_result['clean_data'])
        processing_pipeline['processing_stages'].append('ai_analytics')
        processing_pipeline['results']['analytics'] = analytics_result
        
        # Stage 3: Threat Detection
        threat_result = self.services['threat_detection'].detect_threats(analytics_result)
        processing_pipeline['processing_stages'].append('threat_detection')
        processing_pipeline['results']['threats'] = threat_result
        
        # Stage 4: Experience Optimization
        optimization_result = self.services['experience_optimization'].optimize(
            analytics_result,
            threat_result
        )
        processing_pipeline['processing_stages'].append('experience_optimization')
        processing_pipeline['results']['optimization'] = optimization_result
        
        # Stage 5: Alerting and Notifications
        if threat_result['high_risk_threats'] or optimization_result['critical_issues']:
            alert_result = self.services['alerting'].generate_alerts(
                threat_result,
                optimization_result
            )
            processing_pipeline['results']['alerts'] = alert_result
        
        return processing_pipeline
    
    def real_time_processing_pipeline(self):
        """Real-time processing pipeline for streaming telemetry"""
        while True:
            try:
                # Consume telemetry from message broker
                telemetry_batch = self.message_broker.consume_batch(
                    topic='user_telemetry',
                    batch_size=1000,
                    timeout=1000
                )
                
                if telemetry_batch:
                    # Process batch through AI pipeline
                    batch_results = []
                    for telemetry in telemetry_batch:
                        result = self.process_user_telemetry(telemetry)
                        batch_results.append(result)
                    
                    # Store results in data lake
                    self.data_lake.store_batch_results(batch_results)
                    
                    # Update ML models with new data
                    self.ml_pipeline.update_models(batch_results)
                
            except Exception as e:
                logger.error(f"Error in real-time processing pipeline: {e}")
                time.sleep(5)  # Brief pause before retrying

class EdgeAIDEMProcessor:
    def __init__(self):
        self.edge_models = {
            'anomaly_detection': LightweightAnomalyModel(),
            'threat_classification': EdgeThreatClassifier(),
            'performance_prediction': EdgePerformancePredictor()
        }
        self.cloud_sync = CloudSynchronizationService()
        self.local_storage = EdgeDataStorage()
    
    def process_at_edge(self, user_interaction):
        """Process user interactions at the edge for low latency"""
        edge_analysis = {
            'interaction_id': user_interaction['id'],
            'timestamp': datetime.utcnow(),
            'edge_processing': True,
            'latency_ms': 0,
            'analysis_results': {}
        }
        
        start_time = time.time()
        
        # Run lightweight anomaly detection
        anomaly_result = self.edge_models['anomaly_detection'].detect(user_interaction)
        edge_analysis['analysis_results']['anomaly'] = anomaly_result
        
        # Run threat classification if anomaly detected
        if anomaly_result['is_anomaly']:
            threat_result = self.edge_models['threat_classification'].classify(user_interaction)
            edge_analysis['analysis_results']['threat'] = threat_result
            
            # If high-risk threat, send to cloud immediately
            if threat_result['risk_level'] == 'HIGH':
                self.cloud_sync.send_urgent_alert(edge_analysis)
        
        # Run performance prediction
        performance_result = self.edge_models['performance_prediction'].predict(user_interaction)
        edge_analysis['analysis_results']['performance'] = performance_result
        
        # Calculate processing latency
        edge_analysis['latency_ms'] = (time.time() - start_time) * 1000
        
        # Store locally and sync with cloud periodically
        self.local_storage.store(edge_analysis)
        
        return edge_analysis
    
    def sync_with_cloud(self):
        """Synchronize edge data and models with cloud"""
        # Upload local data to cloud
        local_data = self.local_storage.get_unsync_data()
        if local_data:
            self.cloud_sync.upload_edge_data(local_data)
            self.local_storage.mark_as_synced(local_data)
        
        # Download updated models from cloud
        updated_models = self.cloud_sync.get_model_updates()
        if updated_models:
            for model_name, model_data in updated_models.items():
                if model_name in self.edge_models:
                    self.edge_models[model_name].update(model_data)

class PrivacyPreservingAIDEM:
    def __init__(self, epsilon=1.0):
        self.epsilon = epsilon  # Privacy budget
        self.noise_generator = DifferentialPrivacyNoiseGenerator()
        self.privacy_accountant = PrivacyAccountant()
    
    def private_behavioral_analysis(self, user_behaviors):
        """Perform behavioral analysis with differential privacy"""
        # Add calibrated noise to protect individual privacy
        noisy_behaviors = []
        
        for behavior in user_behaviors:
            # Calculate sensitivity of the analysis
            sensitivity = self.calculate_sensitivity(behavior)
            
            # Add Laplace noise for differential privacy
            noise_scale = sensitivity / self.epsilon
            noisy_behavior = self.noise_generator.add_laplace_noise(
                behavior,
                noise_scale
            )
            
            noisy_behaviors.append(noisy_behavior)
        
        # Perform analysis on noisy data
        analysis_result = self.perform_behavioral_analysis(noisy_behaviors)
        
        # Track privacy budget usage
        self.privacy_accountant.consume_budget(self.epsilon)
        
        return {
            'analysis_result': analysis_result,
            'privacy_preserved': True,
            'epsilon_used': self.epsilon,
            'remaining_budget': self.privacy_accountant.get_remaining_budget()
        }
    
    def federated_learning_update(self, local_model_updates):
        """Update global model using federated learning with privacy"""
        # Apply differential privacy to model updates
        private_updates = []
        
        for update in local_model_updates:
            # Clip gradients to bound sensitivity
            clipped_update = self.clip_gradients(update, clip_norm=1.0)
            
            # Add Gaussian noise for privacy
            noise_scale = self.calculate_noise_scale(clip_norm=1.0)
            noisy_update = self.noise_generator.add_gaussian_noise(
                clipped_update,
                noise_scale
            )
            
            private_updates.append(noisy_update)
        
        # Aggregate private updates
        global_update = self.aggregate_updates(private_updates)
        
        return {
            'global_update': global_update,
            'privacy_preserved': True,
            'participants': len(local_model_updates)
        }

class GDPRCompliantAIDEM:
    def __init__(self):
        self.consent_manager = ConsentManager()
        self.data_processor = GDPRDataProcessor()
        self.retention_manager = DataRetentionManager()
        self.audit_logger = ComplianceAuditLogger()
    
    def process_user_data_with_consent(self, user_id, telemetry_data, processing_purpose):
        """Process user data with GDPR compliance checks"""
        compliance_check = {
            'user_id': user_id,
            'processing_purpose': processing_purpose,
            'consent_status': None,
            'lawful_basis': None,
            'processing_allowed': False,
            'data_minimization_applied': False,
            'retention_period': None
        }
        
        # Check user consent
        consent_status = self.consent_manager.check_consent(user_id, processing_purpose)
        compliance_check['consent_status'] = consent_status
        
        if consent_status['has_consent']:
            # Determine lawful basis for processing
            lawful_basis = self.determine_lawful_basis(processing_purpose, consent_status)
            compliance_check['lawful_basis'] = lawful_basis
            
            if lawful_basis:
                # Apply data minimization
                minimized_data = self.data_processor.minimize_data(
                    telemetry_data,
                    processing_purpose
                )
                compliance_check['data_minimization_applied'] = True
                
                # Set retention period
                retention_period = self.retention_manager.get_retention_period(
                    processing_purpose
                )
                compliance_check['retention_period'] = retention_period
                
                # Process data
                processing_result = self.process_telemetry_data(
                    minimized_data,
                    processing_purpose
                )
                
                # Log processing activity
                self.audit_logger.log_processing_activity(
                    user_id,
                    processing_purpose,
                    lawful_basis,
                    len(minimized_data)
                )
                
                compliance_check['processing_allowed'] = True
                compliance_check['processing_result'] = processing_result
        
        return compliance_check
    
    def handle_data_subject_rights(self, user_id, request_type):
        """Handle GDPR data subject rights requests"""
        if request_type == 'access':
            return self.provide_data_access(user_id)
        elif request_type == 'rectification':
            return self.handle_data_rectification(user_id)
        elif request_type == 'erasure':
            return self.handle_data_erasure(user_id)
        elif request_type == 'portability':
            return self.provide_data_portability(user_id)
        elif request_type == 'restriction':
            return self.restrict_data_processing(user_id)
        else:
            raise ValueError(f"Unknown request type: {request_type}")

class QuantumAIDEM:
    def __init__(self):
        self.quantum_processor = QuantumProcessor()
        self.quantum_ml_models = {
            'quantum_svm': QuantumSupportVectorMachine(),
            'quantum_neural_network': QuantumNeuralNetwork(),
            'quantum_clustering': QuantumClustering()
        }
        self.classical_fallback = ClassicalAIDEM()
    
    def quantum_behavioral_analysis(self, user_behavior_data):
        """Perform behavioral analysis using quantum machine learning"""
        if self.quantum_processor.is_available():
            # Encode classical data into quantum states
            quantum_states = self.encode_to_quantum_states(user_behavior_data)
            
            # Run quantum machine learning algorithms
            quantum_results = {}
            
            # Quantum clustering for behavior patterns
            behavior_clusters = self.quantum_ml_models['quantum_clustering'].cluster(
                quantum_states
            )
            quantum_results['behavior_clusters'] = behavior_clusters
            
            # Quantum SVM for anomaly detection
            anomaly_classification = self.quantum_ml_models['quantum_svm'].classify(
                quantum_states
            )
            quantum_results['anomaly_detection'] = anomaly_classification
            
            # Quantum neural network for threat prediction
            threat_prediction = self.quantum_ml_models['quantum_neural_network'].predict(
                quantum_states
            )
            quantum_results['threat_prediction'] = threat_prediction
            
            return {
                'quantum_processing': True,
                'results': quantum_results,
                'quantum_advantage': self.calculate_quantum_advantage(quantum_results)
            }
        else:
            # Fall back to classical processing
            return self.classical_fallback.behavioral_analysis(user_behavior_data)
    
    def quantum_optimization(self, experience_parameters):
        """Optimize user experience using quantum optimization algorithms"""
        # Formulate optimization problem for quantum annealing
        optimization_problem = self.formulate_qubo_problem(experience_parameters)
        
        # Solve using quantum annealing
        quantum_solution = self.quantum_processor.quantum_anneal(optimization_problem)
        
        # Interpret quantum solution
        optimized_parameters = self.interpret_quantum_solution(
            quantum_solution,
            experience_parameters
        )
        
        return {
            'optimized_parameters': optimized_parameters,
            'quantum_optimization': True,
            'solution_quality': quantum_solution['energy']
        }