Autonomous Data Governance

import asyncio
import json
import re
from typing import Dict, List, Any, Optional, Tuple, Set
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import hashlib
from concurrent.futures import ThreadPoolExecutor
import logging

class PolicyType(Enum):
    DATA_CLASSIFICATION = "data_classification"
    ACCESS_CONTROL = "access_control"
    RETENTION = "retention"
    PRIVACY = "privacy"
    QUALITY = "quality"
    LINEAGE = "lineage"

class RiskLevel(Enum):
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    CRITICAL = 4

@dataclass
class PolicyRule:
    id: str
    name: str
    policy_type: PolicyType
    conditions: Dict[str, Any]
    actions: List[Dict[str, Any]]
    risk_level: RiskLevel
    priority: int
    created_at: datetime
    updated_at: datetime
    active: bool = True
    version: int = 1
    conflicts: Set[str] = field(default_factory=set)

@dataclass
class ComplianceRequirement:
    regulation: str  # GDPR, CCPA, HIPAA, etc.
    requirement_id: str
    description: str
    applicable_data_types: List[str]
    mandatory_actions: List[str]
    penalties: Dict[str, Any]
    effective_date: datetime
    
@dataclass
class DataContext:
    data_id: str
    data_type: str
    classification: str
    sensitivity: str
    source: str
    owner: str
    location: str
    access_patterns: Dict[str, Any]
    metadata: Dict[str, Any]

class AutonomousPolicyEngine:
    def __init__(self):
        self.policies: Dict[str, PolicyRule] = {}
        self.compliance_requirements: Dict[str, ComplianceRequirement] = {}
        self.data_contexts: Dict[str, DataContext] = {}
        self.policy_graph: Dict[str, List[str]] = {}
        self.ml_classifier = MLDataClassifier()
        self.regulation_monitor = RegulationMonitor()
        self.violation_tracker = ViolationTracker()
        
        # Performance tracking
        self.metrics = {
            'policies_applied': 0,
            'violations_detected': 0,
            'auto_remediations': 0,
            'policy_conflicts_resolved': 0,
            'avg_policy_evaluation_time': 0.0
        }

    async def initialize_system(self):
        """Initialize the autonomous policy engine with baseline policies"""
        # Load baseline compliance requirements
        await self.load_baseline_compliance_requirements()
        
        # Generate initial policy set
        await self.generate_baseline_policies()
        
        # Start background tasks
        asyncio.create_task(self.monitor_regulatory_changes())
        asyncio.create_task(self.continuous_policy_optimization())
        asyncio.create_task(self.detect_and_resolve_conflicts())
        
        logging.info("Autonomous Policy Engine initialized")

    async def classify_and_govern_data(self, data_id: str, raw_data: Any, 
                                     metadata: Dict[str, Any]) -> Dict[str, Any]:
        """Main entry point for autonomous data governance"""
        start_time = datetime.utcnow()
        
        # Step 1: Classify data using ML
        classification_result = await self.ml_classifier.classify_data(raw_data)
        
        # Step 2: Create data context
        data_context = DataContext(
            data_id=data_id,
            data_type=classification_result['data_type'],
            classification=classification_result['classification'],
            sensitivity=classification_result['sensitivity'],
            source=metadata.get('source', 'unknown'),
            owner=metadata.get('owner', 'unknown'),
            location=metadata.get('location', 'unknown'),
            access_patterns={},
            metadata=metadata
        )
        
        self.data_contexts[data_id] = data_context
        
        # Step 3: Apply relevant policies
        applicable_policies = await self.get_applicable_policies(data_context)
        policy_results = []
        
        for policy in applicable_policies:
            result = await self.apply_policy(policy, data_context)
            policy_results.append(result)
            self.metrics['policies_applied'] += 1
        
        # Step 4: Check for violations
        violations = await self.check_compliance_violations(data_context, policy_results)
        
        # Step 5: Auto-remediate if possible
        remediation_actions = []
        for violation in violations:
            if violation['auto_remediable']:
                action = await self.auto_remediate_violation(violation, data_context)
                remediation_actions.append(action)
                self.metrics['auto_remediations'] += 1
            
        # Update metrics
        processing_time = (datetime.utcnow() - start_time).total_seconds()
        self.metrics['avg_policy_evaluation_time'] =             (self.metrics['avg_policy_evaluation_time'] + processing_time) / 2
        
        return {
            'data_context': data_context,
            'applied_policies': [p.id for p in applicable_policies],
            'policy_results': policy_results,
            'violations': violations,
            'remediation_actions': remediation_actions,
            'governance_score': self.calculate_governance_score(policy_results, violations),
            'processing_time_ms': processing_time * 1000
        }

    async def get_applicable_policies(self, data_context: DataContext) -> List[PolicyRule]:
        """Determine which policies apply to the given data context"""
        applicable = []
        
        for policy in self.policies.values():
            if not policy.active:
                continue
                
            # Check if policy conditions match data context
            if await self.evaluate_policy_conditions(policy.conditions, data_context):
                applicable.append(policy)
        
        # Sort by priority (higher priority first)
        applicable.sort(key=lambda p: p.priority, reverse=True)
        return applicable

    async def evaluate_policy_conditions(self, conditions: Dict[str, Any], 
                                       data_context: DataContext) -> bool:
        """Evaluate if policy conditions match the data context"""
        for condition_type, condition_value in conditions.items():
            if condition_type == 'data_type':
                if not self.matches_pattern(data_context.data_type, condition_value):
                    return False
                    
            elif condition_type == 'classification':
                if data_context.classification not in condition_value:
                    return False
                    
            elif condition_type == 'sensitivity':
                if data_context.sensitivity not in condition_value:
                    return False
                    
            elif condition_type == 'source':
                if not self.matches_pattern(data_context.source, condition_value):
                    return False
                    
            elif condition_type == 'location':
                if not self.matches_pattern(data_context.location, condition_value):
                    return False
                    
            elif condition_type == 'custom_logic':
                # Evaluate custom condition logic
                if not await self.evaluate_custom_logic(condition_value, data_context):
                    return False
        
        return True

    async def apply_policy(self, policy: PolicyRule, 
                          data_context: DataContext) -> Dict[str, Any]:
        """Apply a specific policy to data context"""
        results = {
            'policy_id': policy.id,
            'policy_name': policy.name,
            'applied_actions': [],
            'success': True,
            'errors': []
        }
        
        try:
            for action in policy.actions:
                action_result = await self.execute_policy_action(action, data_context)
                results['applied_actions'].append({
                    'action': action,
                    'result': action_result,
                    'timestamp': datetime.utcnow().isoformat()
                })
        except Exception as e:
            results['success'] = False
            results['errors'].append(str(e))
            logging.error(f"Policy application failed: {e}")
        
        return results

    async def execute_policy_action(self, action: Dict[str, Any], 
                                  data_context: DataContext) -> Dict[str, Any]:
        """Execute a specific policy action"""
        action_type = action.get('type')
        
        if action_type == 'tag_data':
            return await self.tag_data(data_context, action['tags'])
            
        elif action_type == 'restrict_access':
            return await self.restrict_access(data_context, action['restrictions'])
            
        elif action_type == 'encrypt_data':
            return await self.encrypt_data(data_context, action['encryption_spec'])
            
        elif action_type == 'set_retention':
            return await self.set_retention_policy(data_context, action['retention_period'])
            
        elif action_type == 'anonymize_data':
            return await self.anonymize_data(data_context, action['anonymization_spec'])
            
        elif action_type == 'audit_log':
            return await self.create_audit_log(data_context, action['log_spec'])
            
        elif action_type == 'alert':
            return await self.send_alert(data_context, action['alert_spec'])
        
        else:
            raise ValueError(f"Unknown policy action type: {action_type}")

    async def generate_policy_from_requirement(self, requirement: ComplianceRequirement) -> PolicyRule:
        """Automatically generate policy from compliance requirement"""
        policy_id = f"auto_policy_{requirement.regulation}_{requirement.requirement_id}"
        
        # Use NLP to parse requirement description and generate conditions/actions
        conditions, actions = await self.parse_requirement_to_policy(requirement)
        
        policy = PolicyRule(
            id=policy_id,
            name=f"Auto-generated: {requirement.regulation} {requirement.requirement_id}",
            policy_type=self.infer_policy_type(requirement),
            conditions=conditions,
            actions=actions,
            risk_level=self.assess_risk_level(requirement),
            priority=self.calculate_priority(requirement),
            created_at=datetime.utcnow(),
            updated_at=datetime.utcnow(),
            version=1
        )
        
        # Check for conflicts with existing policies
        conflicts = await self.detect_policy_conflicts(policy)
        policy.conflicts = conflicts
        
        return policy

    async def detect_policy_conflicts(self, new_policy: PolicyRule) -> Set[str]:
        """Detect conflicts between new policy and existing policies"""
        conflicts = set()
        
        for existing_id, existing_policy in self.policies.items():
            if await self.policies_conflict(new_policy, existing_policy):
                conflicts.add(existing_id)
        
        return conflicts

    async def policies_conflict(self, policy1: PolicyRule, policy2: PolicyRule) -> bool:
        """Check if two policies have conflicting requirements"""
        # Check for overlapping conditions with contradictory actions
        conditions_overlap = await self.conditions_overlap(
            policy1.conditions, policy2.conditions
        )
        
        if not conditions_overlap:
            return False
        
        # Check if actions contradict each other
        return await self.actions_contradict(policy1.actions, policy2.actions)

    async def auto_resolve_policy_conflicts(self, conflicts: List[Tuple[str, str]]) -> None:
        """Automatically resolve policy conflicts using predefined strategies"""
        for policy1_id, policy2_id in conflicts:
            policy1 = self.policies[policy1_id]
            policy2 = self.policies[policy2_id]
            
            # Strategy 1: Merge compatible policies
            if await self.can_merge_policies(policy1, policy2):
                merged_policy = await self.merge_policies(policy1, policy2)
                self.policies[merged_policy.id] = merged_policy
                del self.policies[policy1_id]
                del self.policies[policy2_id]
                self.metrics['policy_conflicts_resolved'] += 1
                continue
            
            # Strategy 2: Prioritize by risk level and compliance
            if policy1.risk_level.value > policy2.risk_level.value:
                policy1.priority += 10  # Boost higher risk policy
            elif policy2.risk_level.value > policy1.risk_level.value:
                policy2.priority += 10
            
            # Strategy 3: Create conditional policy branches
            await self.create_conditional_branches(policy1, policy2)
            self.metrics['policy_conflicts_resolved'] += 1

    async def monitor_regulatory_changes(self) -> None:
        """Background task to monitor regulatory changes and update policies"""
        while True:
            try:
                changes = await self.regulation_monitor.check_for_updates()
                
                for change in changes:
                    # Generate new policies or update existing ones
                    if change['type'] == 'new_requirement':
                        new_policy = await self.generate_policy_from_requirement(
                            change['requirement']
                        )
                        await self.add_policy(new_policy)
                    
                    elif change['type'] == 'requirement_update':
                        await self.update_policies_for_requirement(change['requirement'])
                    
                    elif change['type'] == 'requirement_removal':
                        await self.deactivate_policies_for_requirement(change['requirement_id'])
                
                logging.info(f"Processed {len(changes)} regulatory changes")
                
            except Exception as e:
                logging.error(f"Error monitoring regulatory changes: {e}")
            
            # Check every hour
            await asyncio.sleep(3600)

    def calculate_governance_score(self, policy_results: List[Dict[str, Any]], 
                                 violations: List[Dict[str, Any]]) -> float:
        """Calculate overall governance score for data"""
        if not policy_results:
            return 0.0
        
        # Base score from successful policy applications
        successful_policies = sum(1 for result in policy_results if result['success'])
        base_score = (successful_policies / len(policy_results)) * 100
        
        # Penalty for violations
        violation_penalty = len(violations) * 10
        
        # Bonus for high-risk policy compliance
        high_risk_bonus = sum(
            5 for result in policy_results 
            if result['success'] and 'high_risk' in result.get('policy_name', '').lower()
        )
        
        final_score = max(0, base_score - violation_penalty + high_risk_bonus)
        return min(100, final_score)

    async def get_governance_metrics(self) -> Dict[str, Any]:
        """Get current governance performance metrics"""
        total_data_items = len(self.data_contexts)
        active_policies = sum(1 for p in self.policies.values() if p.active)
        
        return {
            **self.metrics,
            'total_data_items': total_data_items,
            'active_policies': active_policies,
            'compliance_requirements': len(self.compliance_requirements),
            'avg_governance_score': await self.calculate_avg_governance_score(),
            'policy_conflict_rate': len([p for p in self.policies.values() if p.conflicts]) / max(1, active_policies),
            'auto_remediation_rate': self.metrics['auto_remediations'] / max(1, self.metrics['violations_detected'])
        }

    # Utility methods (simplified implementations)
    
    def matches_pattern(self, value: str, pattern: Any) -> bool:
        """Check if value matches pattern (string or regex)"""
        if isinstance(pattern, str):
            return value == pattern
        elif isinstance(pattern, list):
            return value in pattern
        elif isinstance(pattern, dict) and 'regex' in pattern:
            return bool(re.match(pattern['regex'], value))
        return False

    async def evaluate_custom_logic(self, logic: Dict[str, Any], 
                                  context: DataContext) -> bool:
        """Evaluate custom condition logic"""
        # This would implement a secure expression evaluator
        # For now, return True as placeholder
        return True

    async def tag_data(self, context: DataContext, tags: List[str]) -> Dict[str, Any]:
        """Tag data with governance labels"""
        context.metadata['governance_tags'] = tags
        return {'tagged': True, 'tags': tags}

    async def restrict_access(self, context: DataContext, restrictions: Dict[str, Any]) -> Dict[str, Any]:
        """Apply access restrictions to data"""
        context.metadata['access_restrictions'] = restrictions
        return {'restricted': True, 'restrictions': restrictions}

    async def load_baseline_compliance_requirements(self) -> None:
        """Load baseline compliance requirements"""
        # This would load from configuration or external systems
        pass

    async def generate_baseline_policies(self) -> None:
        """Generate baseline policy set"""
        # This would create initial policy set based on requirements
        pass

# Simplified supporting classes

class MLDataClassifier:
    async def classify_data(self, data: Any) -> Dict[str, Any]:
        """Classify data using machine learning"""
        # Placeholder implementation
        return {
            'data_type': 'user_data',
            'classification': 'confidential',
            'sensitivity': 'high',
            'confidence_score': 0.95
        }

class RegulationMonitor:
    async def check_for_updates(self) -> List[Dict[str, Any]]:
        """Check for regulatory updates"""
        # Placeholder - would integrate with legal databases
        return []

class ViolationTracker:
    def __init__(self):
        self.violations: List[Dict[str, Any]] = []

# Usage example
async def demonstrate_autonomous_governance():
    engine = AutonomousPolicyEngine()
    await engine.initialize_system()
    
    # Simulate data ingestion with autonomous governance
    test_data = {
        'user_id': '123456',
        'email': 'user@example.com',
        'ssn': '123-45-6789',
        'address': '123 Main St, Anytown, USA'
    }
    
    metadata = {
        'source': 'user_registration_form',
        'owner': 'marketing_team',
        'location': 'us-east-1'
    }
    
    # Apply autonomous governance
    result = await engine.classify_and_govern_data(
        'data_001', 
        test_data, 
        metadata
    )
    
    print(f"Governance applied: {len(result['applied_policies'])} policies")
    print(f"Governance score: {result['governance_score']}")
    print(f"Violations detected: {len(result['violations'])}")
    print(f"Auto-remediations: {len(result['remediation_actions'])}")
    
    # Get system metrics
    metrics = await engine.get_governance_metrics()
    print(f"System metrics: {json.dumps(metrics, indent=2)}")

if __name__ == "__main__":
    asyncio.run(demonstrate_autonomous_governance())

import { EventEmitter } from 'events';
import axios from 'axios';

interface ComplianceRule {
  id: string;
  regulation: string;
  requirement: string;
  dataTypes: string[];
  actions: ComplianceAction[];
  severity: 'low' | 'medium' | 'high' | 'critical';
  effectiveDate: Date;
  lastUpdated: Date;
}

interface ComplianceAction {
  type: 'encrypt' | 'anonymize' | 'delete' | 'restrict' | 'audit' | 'notify';
  parameters: Record<string, any>;
  deadline?: number; // milliseconds
  mandatory: boolean;
}

interface DataRecord {
  id: string;
  type: string;
  content: any;
  metadata: Record<string, any>;
  classification: {
    sensitivity: 'public' | 'internal' | 'confidential' | 'restricted';
    dataTypes: string[];
    personalData: boolean;
    geolocation?: string;
  };
  accessHistory: AccessEvent[];
  lastModified: Date;
}

interface AccessEvent {
  userId: string;
  action: string;
  timestamp: Date;
  ipAddress?: string;
  userAgent?: string;
  purpose?: string;
}

interface ComplianceViolation {
  id: string;
  ruleId: string;
  dataRecordId: string;
  violationType: string;
  severity: 'low' | 'medium' | 'high' | 'critical';
  description: string;
  detectedAt: Date;
  autoRemediable: boolean;
  estimatedFine?: number;
  requiresHumanReview: boolean;
}

interface RegulatoryUpdate {
  regulation: string;
  changeType: 'new_rule' | 'rule_modification' | 'rule_removal' | 'interpretation_change';
  effectiveDate: Date;
  summary: string;
  impactedDataTypes: string[];
  requiredActions: string[];
  confidenceScore: number;
}

class SmartComplianceMonitor extends EventEmitter {
  private rules: Map<string, ComplianceRule> = new Map();
  private dataRecords: Map<string, DataRecord> = new Map();
  private violations: Map<string, ComplianceViolation> = new Map();
  private processingQueue: Array<{ dataId: string; operation: string }> = [];
  private isProcessing = false;
  
  // ML-powered components
  private riskScorer: RiskAssessmentEngine;
  private patternDetector: AnomalyDetector;
  private regulatoryScanner: RegulatoryChangeScanner;
  
  // Performance metrics
  private metrics = {
    recordsProcessed: 0,
    violationsDetected: 0,
    autoRemediations: 0,
    avgProcessingTime: 0,
    complianceScore: 100,
    falsePositives: 0
  };

  constructor() {
    super();
    this.riskScorer = new RiskAssessmentEngine();
    this.patternDetector = new AnomalyDetector();
    this.regulatoryScanner = new RegulatoryChangeScanner();
    
    // Start background monitoring
    this.startContinuousMonitoring();
    this.startRegulatoryMonitoring();
  }

  async processDataRecord(record: DataRecord): Promise<{
    compliant: boolean;
    violations: ComplianceViolation[];
    actions: ComplianceAction[];
    riskScore: number;
  }> {
    const startTime = Date.now();
    const violations: ComplianceViolation[] = [];
    const actions: ComplianceAction[] = [];

    try {
      // Step 1: Classify and analyze data
      const classification = await this.classifyData(record);
      record.classification = { ...record.classification, ...classification };

      // Step 2: Apply relevant compliance rules
      const applicableRules = this.getApplicableRules(record);
      
      for (const rule of applicableRules) {
        const ruleViolations = await this.checkRuleCompliance(record, rule);
        violations.push(...ruleViolations);
        
        // Determine required actions
        if (ruleViolations.length > 0) {
          actions.push(...this.determineRequiredActions(rule, ruleViolations));
        }
      }

      // Step 3: Assess risk score
      const riskScore = await this.riskScorer.assessRisk(record, violations);

      // Step 4: Detect anomalous patterns
      const anomalies = await this.patternDetector.detectAnomalies(record);
      if (anomalies.length > 0) {
        const anomalyViolations = this.convertAnomaliesToViolations(anomalies);
        violations.push(...anomalyViolations);
      }

      // Step 5: Auto-remediate if possible
      await this.attemptAutoRemediation(record, violations, actions);

      // Update metrics
      this.updateMetrics(startTime, violations.length > 0);
      
      // Store violations for tracking
      violations.forEach(violation => {
        this.violations.set(violation.id, violation);
      });

      // Emit events for downstream processing
      if (violations.length > 0) {
        this.emit('violationDetected', { record, violations, riskScore });
      }

      return {
        compliant: violations.length === 0,
        violations,
        actions,
        riskScore
      };

    } catch (error) {
      this.emit('processingError', { record, error });
      throw error;
    }
  }

  private async classifyData(record: DataRecord): Promise<Partial<DataRecord['classification']>> {
    const classification: Partial<DataRecord['classification']> = {};

    // Detect personal data using pattern matching and ML
    const personalDataPatterns = [
      /\b\d{3}-\d{2}-\d{4}\b/, // SSN
      /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b/, // Email
      /\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b/, // Credit Card
      /\+?1?[\s-]?\(?\d{3}\)?[\s-]?\d{3}[\s-]?\d{4}\b/ // Phone
    ];

    const contentStr = JSON.stringify(record.content);
    const hasPersonalData = personalDataPatterns.some(pattern => pattern.test(contentStr));
    classification.personalData = hasPersonalData;

    // Detect specific data types
    const detectedTypes: string[] = [];
    if (/ssn|social.security/i.test(contentStr)) detectedTypes.push('ssn');
    if (/@/.test(contentStr)) detectedTypes.push('email');
    if (/credit.card|card.number/i.test(contentStr)) detectedTypes.push('credit_card');
    if (/phone|telephone/i.test(contentStr)) detectedTypes.push('phone');
    if (/address|street|city|zip/i.test(contentStr)) detectedTypes.push('address');
    if (/medical|health|patient/i.test(contentStr)) detectedTypes.push('medical');

    classification.dataTypes = detectedTypes;

    // Assess sensitivity level
    if (detectedTypes.includes('ssn') || detectedTypes.includes('credit_card') || detectedTypes.includes('medical')) {
      classification.sensitivity = 'restricted';
    } else if (hasPersonalData) {
      classification.sensitivity = 'confidential';
    } else if (detectedTypes.length > 0) {
      classification.sensitivity = 'internal';
    } else {
      classification.sensitivity = 'public';
    }

    return classification;
  }

  private getApplicableRules(record: DataRecord): ComplianceRule[] {
    const applicable: ComplianceRule[] = [];

    for (const rule of this.rules.values()) {
      // Check if rule applies to this data type
      const dataTypeMatch = rule.dataTypes.some(type => 
        record.classification.dataTypes?.includes(type) || record.type === type
      );

      // Check if rule is currently effective
      const isEffective = rule.effectiveDate <= new Date();

      if (dataTypeMatch && isEffective) {
        applicable.push(rule);
      }
    }

    // Sort by severity (critical first)
    return applicable.sort((a, b) => {
      const severityOrder = { critical: 4, high: 3, medium: 2, low: 1 };
      return severityOrder[b.severity] - severityOrder[a.severity];
    });
  }

  private async checkRuleCompliance(record: DataRecord, rule: ComplianceRule): Promise<ComplianceViolation[]> {
    const violations: ComplianceViolation[] = [];

    // GDPR-specific checks
    if (rule.regulation === 'GDPR') {
      // Right to be forgotten
      if (rule.requirement.includes('right_to_erasure')) {
        const retentionViolation = this.checkDataRetention(record, rule);
        if (retentionViolation) violations.push(retentionViolation);
      }

      // Consent checks
      if (rule.requirement.includes('consent') && record.classification.personalData) {
        const consentViolation = this.checkConsent(record, rule);
        if (consentViolation) violations.push(consentViolation);
      }

      // Data minimization
      if (rule.requirement.includes('data_minimization')) {
        const minimizationViolation = this.checkDataMinimization(record, rule);
        if (minimizationViolation) violations.push(minimizationViolation);
      }
    }

    // CCPA-specific checks
    if (rule.regulation === 'CCPA') {
      // Right to know
      if (rule.requirement.includes('right_to_know')) {
        const transparencyViolation = this.checkTransparency(record, rule);
        if (transparencyViolation) violations.push(transparencyViolation);
      }

      // Do not sell
      if (rule.requirement.includes('do_not_sell')) {
        const saleViolation = this.checkSaleRestrictions(record, rule);
        if (saleViolation) violations.push(saleViolation);
      }
    }

    // HIPAA-specific checks
    if (rule.regulation === 'HIPAA' && record.classification.dataTypes?.includes('medical')) {
      // Minimum necessary standard
      if (rule.requirement.includes('minimum_necessary')) {
        const accessViolation = this.checkMinimumNecessary(record, rule);
        if (accessViolation) violations.push(accessViolation);
      }

      // Encryption requirements
      if (rule.requirement.includes('encryption')) {
        const encryptionViolation = this.checkEncryption(record, rule);
        if (encryptionViolation) violations.push(encryptionViolation);
      }
    }

    return violations;
  }

  private checkDataRetention(record: DataRecord, rule: ComplianceRule): ComplianceViolation | null {
    const retentionPeriod = rule.actions.find(a => a.type === 'delete')?.parameters.retentionDays || 365;
    const daysSinceCreation = (Date.now() - record.lastModified.getTime()) / (1000 * 60 * 60 * 24);

    if (daysSinceCreation > retentionPeriod) {
      return {
        id: `retention_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
        ruleId: rule.id,
        dataRecordId: record.id,
        violationType: 'data_retention_exceeded',
        severity: rule.severity,
        description: `Data retained beyond ${retentionPeriod} days (${Math.floor(daysSinceCreation)} days)`,
        detectedAt: new Date(),
        autoRemediable: true,
        estimatedFine: this.calculatePotentialFine(rule.regulation, rule.severity),
        requiresHumanReview: false
      };
    }

    return null;
  }

  private checkConsent(record: DataRecord, rule: ComplianceRule): ComplianceViolation | null {
    const hasConsent = record.metadata.consent?.given === true;
    const consentDate = record.metadata.consent?.timestamp;

    if (!hasConsent) {
      return {
        id: `consent_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
        ruleId: rule.id,
        dataRecordId: record.id,
        violationType: 'missing_consent',
        severity: 'critical',
        description: 'Personal data processed without valid consent',
        detectedAt: new Date(),
        autoRemediable: false,
        estimatedFine: this.calculatePotentialFine('GDPR', 'critical'),
        requiresHumanReview: true
      };
    }

    // Check consent expiry (if applicable)
    if (consentDate && rule.actions.some(a => a.parameters.consentExpiryDays)) {
      const expiryDays = rule.actions.find(a => a.parameters.consentExpiryDays)?.parameters.consentExpiryDays || 365;
      const daysSinceConsent = (Date.now() - new Date(consentDate).getTime()) / (1000 * 60 * 60 * 24);

      if (daysSinceConsent > expiryDays) {
        return {
          id: `consent_expired_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
          ruleId: rule.id,
          dataRecordId: record.id,
          violationType: 'expired_consent',
          severity: 'high',
          description: `Consent expired ${Math.floor(daysSinceConsent - expiryDays)} days ago`,
          detectedAt: new Date(),
          autoRemediable: false,
          requiresHumanReview: true
        };
      }
    }

    return null;
  }

  private async attemptAutoRemediation(
    record: DataRecord,
    violations: ComplianceViolation[],
    actions: ComplianceAction[]
  ): Promise<void> {
    for (const violation of violations) {
      if (!violation.autoRemediable) continue;

      try {
        switch (violation.violationType) {
          case 'data_retention_exceeded':
            await this.autoDelete(record);
            violation.autoRemediable = false; // Mark as remediated
            this.metrics.autoRemediations++;
            break;

          case 'unencrypted_sensitive_data':
            await this.autoEncrypt(record);
            violation.autoRemediable = false;
            this.metrics.autoRemediations++;
            break;

          case 'overly_broad_access':
            await this.restrictAccess(record);
            violation.autoRemediable = false;
            this.metrics.autoRemediations++;
            break;
        }

        this.emit('autoRemediation', { violation, record });
      } catch (error) {
        this.emit('remediationFailed', { violation, record, error });
      }
    }
  }

  private async startRegulatoryMonitoring(): Promise<void> {
    setInterval(async () => {
      try {
        const updates = await this.regulatoryScanner.scanForUpdates();
        
        for (const update of updates) {
          await this.processRegulatoryUpdate(update);
        }
      } catch (error) {
        this.emit('regulatoryMonitoringError', error);
      }
    }, 24 * 60 * 60 * 1000); // Check daily
  }

  private async processRegulatoryUpdate(update: RegulatoryUpdate): Promise<void> {
    // Generate new rules from regulatory updates
    if (update.changeType === 'new_rule' && update.confidenceScore > 0.8) {
      const newRule = await this.generateRuleFromUpdate(update);
      this.rules.set(newRule.id, newRule);
      
      this.emit('newRuleGenerated', { rule: newRule, update });
    }

    // Modify existing rules
    if (update.changeType === 'rule_modification') {
      const affectedRules = Array.from(this.rules.values())
        .filter(rule => rule.regulation === update.regulation);
      
      for (const rule of affectedRules) {
        await this.updateRuleForRegulatoryChange(rule, update);
      }
    }

    this.emit('regulatoryUpdateProcessed', update);
  }

  private calculatePotentialFine(regulation: string, severity: string): number {
    // Simplified fine calculation based on regulation and severity
    const fineStructure: Record<string, Record<string, number>> = {
      'GDPR': { low: 10000, medium: 100000, high: 1000000, critical: 20000000 },
      'CCPA': { low: 2500, medium: 25000, high: 250000, critical: 7500 },
      'HIPAA': { low: 10000, medium: 50000, high: 250000, critical: 1500000 }
    };

    return fineStructure[regulation]?.[severity] || 0;
  }

  async getComplianceReport(): Promise<{
    overallScore: number;
    violationsSummary: Record<string, number>;
    riskAssessment: string;
    recommendedActions: string[];
    metrics: typeof this.metrics;
  }> {
    const violationsSummary: Record<string, number> = {};
    const criticalViolations = Array.from(this.violations.values())
      .filter(v => v.severity === 'critical').length;
    
    // Count violations by type
    for (const violation of this.violations.values()) {
      violationsSummary[violation.violationType] = 
        (violationsSummary[violation.violationType] || 0) + 1;
    }

    // Calculate overall compliance score
    const totalViolations = this.violations.size;
    const scoreDeduction = Math.min(80, totalViolations * 2 + criticalViolations * 10);
    const overallScore = Math.max(0, 100 - scoreDeduction);

    // Risk assessment
    let riskAssessment = 'Low';
    if (criticalViolations > 5) riskAssessment = 'Critical';
    else if (criticalViolations > 0 || totalViolations > 20) riskAssessment = 'High';
    else if (totalViolations > 5) riskAssessment = 'Medium';

    // Generate recommendations
    const recommendedActions = this.generateRecommendations(violationsSummary, riskAssessment);

    return {
      overallScore,
      violationsSummary,
      riskAssessment,
      recommendedActions,
      metrics: { ...this.metrics, complianceScore: overallScore }
    };
  }

  private generateRecommendations(violations: Record<string, number>, riskLevel: string): string[] {
    const recommendations: string[] = [];

    if (violations['missing_consent'] > 0) {
      recommendations.push('Implement consent management platform');
      recommendations.push('Review and update privacy policies');
    }

    if (violations['data_retention_exceeded'] > 0) {
      recommendations.push('Establish automated data retention policies');
      recommendations.push('Implement data lifecycle management');
    }

    if (violations['unencrypted_sensitive_data'] > 0) {
      recommendations.push('Enable encryption for all sensitive data');
      recommendations.push('Review data classification and handling procedures');
    }

    if (riskLevel === 'Critical' || riskLevel === 'High') {
      recommendations.push('Conduct immediate compliance audit');
      recommendations.push('Engage legal counsel for risk assessment');
    }

    return recommendations;
  }

  // Simplified utility methods
  private updateMetrics(startTime: number, hadViolations: boolean): void {
    const processingTime = Date.now() - startTime;
    this.metrics.recordsProcessed++;
    this.metrics.avgProcessingTime = 
      (this.metrics.avgProcessingTime + processingTime) / 2;
    
    if (hadViolations) {
      this.metrics.violationsDetected++;
    }
  }

  private startContinuousMonitoring(): void {
    setInterval(() => this.processQueue(), 1000);
  }

  private async processQueue(): Promise<void> {
    if (this.isProcessing || this.processingQueue.length === 0) return;
    
    this.isProcessing = true;
    try {
      while (this.processingQueue.length > 0) {
        const item = this.processingQueue.shift()!;
        const record = this.dataRecords.get(item.dataId);
        if (record) {
          await this.processDataRecord(record);
        }
      }
    } finally {
      this.isProcessing = false;
    }
  }

  // Additional utility methods would be implemented here...
  private async autoDelete(record: DataRecord): Promise<void> { /* Implementation */ }
  private async autoEncrypt(record: DataRecord): Promise<void> { /* Implementation */ }
  private async restrictAccess(record: DataRecord): Promise<void> { /* Implementation */ }
  private checkDataMinimization(record: DataRecord, rule: ComplianceRule): ComplianceViolation | null { return null; }
  private checkTransparency(record: DataRecord, rule: ComplianceRule): ComplianceViolation | null { return null; }
  private checkSaleRestrictions(record: DataRecord, rule: ComplianceRule): ComplianceViolation | null { return null; }
  private checkMinimumNecessary(record: DataRecord, rule: ComplianceRule): ComplianceViolation | null { return null; }
  private checkEncryption(record: DataRecord, rule: ComplianceRule): ComplianceViolation | null { return null; }
  private determineRequiredActions(rule: ComplianceRule, violations: ComplianceViolation[]): ComplianceAction[] { return []; }
  private convertAnomaliesToViolations(anomalies: any[]): ComplianceViolation[] { return []; }
  private async generateRuleFromUpdate(update: RegulatoryUpdate): Promise<ComplianceRule> { return {} as ComplianceRule; }
  private async updateRuleForRegulatoryChange(rule: ComplianceRule, update: RegulatoryUpdate): Promise<void> { }
}

// Simplified supporting classes
class RiskAssessmentEngine {
  async assessRisk(record: DataRecord, violations: ComplianceViolation[]): Promise<number> {
    return Math.min(100, violations.length * 20);
  }
}

class AnomalyDetector {
  async detectAnomalies(record: DataRecord): Promise<any[]> {
    return [];
  }
}

class RegulatoryChangeScanner {
  async scanForUpdates(): Promise<RegulatoryUpdate[]> {
    return [];
  }
}