Securing Digital Media: Advanced Cybersecurity Strategies for Communications and Information Services

import hashlib
import jwt
import time
from datetime import datetime, timedelta
from cryptography.fernet import Fernet
from typing import Dict, List, Optional, Union
import json
import asyncio
from dataclasses import dataclass
from enum import Enum

class SecurityLevel(Enum):
    PUBLIC = "public"
    INTERNAL = "internal"
    CONFIDENTIAL = "confidential"
    TOP_SECRET = "top_secret"

class ContentType(Enum):
    VIDEO = "video"
    AUDIO = "audio"
    IMAGE = "image"
    DOCUMENT = "document"
    LIVE_STREAM = "live_stream"

@dataclass
class MediaAsset:
    asset_id: str
    title: str
    content_type: ContentType
    security_level: SecurityLevel
    file_path: str
    metadata: Dict
    created_at: datetime
    updated_at: datetime

@dataclass
class AccessRequest:
    user_id: str
    asset_id: str
    requested_permissions: List[str]
    request_time: datetime
    ip_address: str
    user_agent: str
    geolocation: Optional[Dict]

class MediaSecurityManager:
    def __init__(self, encryption_key: bytes, jwt_secret: str):
        self.encryption_key = encryption_key
        self.jwt_secret = jwt_secret
        self.cipher_suite = Fernet(encryption_key)
        self.assets = {}
        self.access_logs = []
        self.watermark_registry = {}
        self.threat_intelligence = {}
        self.security_policies = self._load_default_policies()
        
    def _load_default_policies(self) -> Dict:
        """Load default security policies for different content types"""
        return {
            SecurityLevel.PUBLIC: {
                'encryption_required': False,
                'watermarking_required': False,
                'access_logging': True,
                'geographic_restrictions': [],
                'max_concurrent_streams': 1000
            },
            SecurityLevel.INTERNAL: {
                'encryption_required': True,
                'watermarking_required': True,
                'access_logging': True,
                'geographic_restrictions': ['internal_networks'],
                'max_concurrent_streams': 100
            },
            SecurityLevel.CONFIDENTIAL: {
                'encryption_required': True,
                'watermarking_required': True,
                'access_logging': True,
                'geographic_restrictions': ['approved_countries'],
                'max_concurrent_streams': 10,
                'requires_mfa': True
            },
            SecurityLevel.TOP_SECRET: {
                'encryption_required': True,
                'watermarking_required': True,
                'access_logging': True,
                'geographic_restrictions': ['secure_facilities'],
                'max_concurrent_streams': 1,
                'requires_mfa': True,
                'requires_biometric': True,
                'session_timeout_minutes': 30
            }
        }
    
    def register_media_asset(self, asset: MediaAsset) -> Dict:
        """Register and secure a media asset"""
        # Generate unique asset fingerprint
        asset_fingerprint = self._generate_asset_fingerprint(asset)
        
        # Apply security measures based on classification
        security_config = self.security_policies[asset.security_level]
        
        secured_asset = {
            'asset': asset,
            'fingerprint': asset_fingerprint,
            'security_config': security_config,
            'encryption_status': 'pending',
            'watermark_status': 'pending',
            'access_count': 0,
            'last_accessed': None,
            'integrity_hash': None
        }
        
        # Apply encryption if required
        if security_config['encryption_required']:
            secured_asset['encryption_status'] = 'encrypted'
            secured_asset['encrypted_metadata'] = self._encrypt_sensitive_metadata(asset.metadata)
        
        # Apply watermarking if required
        if security_config['watermarking_required']:
            watermark_id = self._apply_watermark(asset)
            secured_asset['watermark_id'] = watermark_id
            secured_asset['watermark_status'] = 'applied'
        
        # Generate integrity hash
        secured_asset['integrity_hash'] = self._generate_integrity_hash(asset)
        
        # Store secured asset
        self.assets[asset.asset_id] = secured_asset
        
        return {
            'status': 'registered',
            'asset_id': asset.asset_id,
            'fingerprint': asset_fingerprint,
            'security_level': asset.security_level.value,
            'protection_measures': list(security_config.keys())
        }
    
    def _generate_asset_fingerprint(self, asset: MediaAsset) -> str:
        """Generate unique fingerprint for content integrity verification"""
        content_hash = hashlib.sha256()
        content_hash.update(asset.asset_id.encode())
        content_hash.update(asset.title.encode())
        content_hash.update(str(asset.created_at).encode())
        content_hash.update(json.dumps(asset.metadata, sort_keys=True).encode())
        return content_hash.hexdigest()
    
    def _encrypt_sensitive_metadata(self, metadata: Dict) -> bytes:
        """Encrypt sensitive metadata fields"""
        sensitive_data = json.dumps(metadata, sort_keys=True)
        return self.cipher_suite.encrypt(sensitive_data.encode())
    
    def _apply_watermark(self, asset: MediaAsset) -> str:
        """Apply digital watermark to content"""
        watermark_id = f"wm_{asset.asset_id}_{int(time.time())}"
        
        # Watermark configuration based on content type
        watermark_config = {
            ContentType.VIDEO: {
                'type': 'invisible_video',
                'strength': 0.8,
                'frequency': 'per_frame',
                'payload': {'asset_id': asset.asset_id, 'timestamp': datetime.now().isoformat()}
            },
            ContentType.AUDIO: {
                'type': 'spread_spectrum',
                'strength': 0.6,
                'frequency': '44100Hz',
                'payload': {'asset_id': asset.asset_id, 'timestamp': datetime.now().isoformat()}
            },
            ContentType.IMAGE: {
                'type': 'lsb_embedding',
                'strength': 0.9,
                'channels': ['red', 'green', 'blue'],
                'payload': {'asset_id': asset.asset_id, 'timestamp': datetime.now().isoformat()}
            }
        }
        
        config = watermark_config.get(asset.content_type, {})
        
        # Register watermark
        self.watermark_registry[watermark_id] = {
            'asset_id': asset.asset_id,
            'config': config,
            'created_at': datetime.now(),
            'status': 'active'
        }
        
        return watermark_id
    
    def _generate_integrity_hash(self, asset: MediaAsset) -> str:
        """Generate integrity hash for tamper detection"""
        integrity_data = f"{asset.asset_id}:{asset.title}:{asset.file_path}:{asset.created_at}"
        return hashlib.sha256(integrity_data.encode()).hexdigest()
    
    def validate_access_request(self, access_request: AccessRequest) -> Dict:
        """Validate and process access request with security checks"""
        asset = self.assets.get(access_request.asset_id)
        if not asset:
            return {'status': 'denied', 'reason': 'Asset not found'}
        
        security_config = asset['security_config']
        validation_results = {
            'user_id': access_request.user_id,
            'asset_id': access_request.asset_id,
            'timestamp': access_request.request_time,
            'checks': {},
            'status': 'pending'
        }
        
        # Geographic restriction check
        if security_config.get('geographic_restrictions'):
            geo_check = self._validate_geographic_access(
                access_request.geolocation,
                security_config['geographic_restrictions']
            )
            validation_results['checks']['geographic'] = geo_check
            if not geo_check['allowed']:
                validation_results['status'] = 'denied'
                validation_results['reason'] = 'Geographic restriction'
                return validation_results
        
        # Concurrent stream limit check
        concurrent_check = self._check_concurrent_streams(
            access_request.user_id,
            security_config['max_concurrent_streams']
        )
        validation_results['checks']['concurrent_streams'] = concurrent_check
        if not concurrent_check['allowed']:
            validation_results['status'] = 'denied'
            validation_results['reason'] = 'Concurrent stream limit exceeded'
            return validation_results
        
        # Multi-factor authentication check
        if security_config.get('requires_mfa'):
            mfa_check = self._validate_mfa(access_request.user_id)
            validation_results['checks']['mfa'] = mfa_check
            if not mfa_check['valid']:
                validation_results['status'] = 'requires_mfa'
                validation_results['reason'] = 'Multi-factor authentication required'
                return validation_results
        
        # Biometric authentication check
        if security_config.get('requires_biometric'):
            bio_check = self._validate_biometric(access_request.user_id)
            validation_results['checks']['biometric'] = bio_check
            if not bio_check['valid']:
                validation_results['status'] = 'requires_biometric'
                validation_results['reason'] = 'Biometric authentication required'
                return validation_results
        
        # All checks passed
        validation_results['status'] = 'approved'
        
        # Generate secure access token
        access_token = self._generate_access_token(access_request, asset)
        validation_results['access_token'] = access_token
        
        # Log access
        self._log_access(access_request, validation_results)
        
        # Update asset access metrics
        asset['access_count'] += 1
        asset['last_accessed'] = datetime.now()
        
        return validation_results
    
    def _validate_geographic_access(self, user_location: Optional[Dict], restrictions: List[str]) -> Dict:
        """Validate geographic access restrictions"""
        if not user_location or not restrictions:
            return {'allowed': True, 'reason': 'No restrictions'}
        
        user_country = user_location.get('country', '').lower()
        
        # Check against restriction list
        if 'approved_countries' in restrictions:
            approved = ['us', 'ca', 'uk', 'de', 'fr', 'jp']  # Example list
            allowed = user_country in approved
        elif 'internal_networks' in restrictions:
            # Check if IP is from internal network ranges
            allowed = self._is_internal_ip(user_location.get('ip_address'))
        elif 'secure_facilities' in restrictions:
            # Check if access is from secure facility
            allowed = self._is_secure_facility(user_location)
        else:
            allowed = True
        
        return {
            'allowed': allowed,
            'user_country': user_country,
            'restrictions': restrictions,
            'reason': 'Geographic validation completed'
        }
    
    def _check_concurrent_streams(self, user_id: str, max_streams: int) -> Dict:
        """Check concurrent stream limits for user"""
        # Count active streams for user (simplified implementation)
        active_streams = len([log for log in self.access_logs[-100:] 
                            if log.get('user_id') == user_id and 
                            log.get('status') == 'streaming'])
        
        return {
            'allowed': active_streams < max_streams,
            'current_streams': active_streams,
            'max_streams': max_streams,
            'remaining': max(0, max_streams - active_streams)
        }
    
    def _validate_mfa(self, user_id: str) -> Dict:
        """Validate multi-factor authentication status"""
        # Simplified MFA validation (in production, integrate with MFA provider)
        return {
            'valid': True,  # Assume valid for demo
            'method': 'totp',
            'last_verified': datetime.now() - timedelta(minutes=5)
        }
    
    def _validate_biometric(self, user_id: str) -> Dict:
        """Validate biometric authentication"""
        # Simplified biometric validation
        return {
            'valid': True,  # Assume valid for demo
            'method': 'fingerprint',
            'confidence_score': 0.95
        }
    
    def _generate_access_token(self, access_request: AccessRequest, asset: Dict) -> str:
        """Generate secure JWT access token with embedded security controls"""
        payload = {
            'user_id': access_request.user_id,
            'asset_id': access_request.asset_id,
            'permissions': access_request.requested_permissions,
            'security_level': asset['asset'].security_level.value,
            'issued_at': datetime.now().timestamp(),
            'expires_at': (datetime.now() + timedelta(hours=24)).timestamp(),
            'ip_address': access_request.ip_address,
            'watermark_id': asset.get('watermark_id'),
            'integrity_hash': asset['integrity_hash']
        }
        
        # Add session timeout for high-security content
        if asset['security_config'].get('session_timeout_minutes'):
            payload['session_timeout'] = asset['security_config']['session_timeout_minutes']
        
        return jwt.encode(payload, self.jwt_secret, algorithm='HS256')
    
    def _log_access(self, access_request: AccessRequest, validation_result: Dict):
        """Log access attempt for security monitoring"""
        log_entry = {
            'timestamp': datetime.now().isoformat(),
            'user_id': access_request.user_id,
            'asset_id': access_request.asset_id,
            'ip_address': access_request.ip_address,
            'user_agent': access_request.user_agent,
            'geolocation': access_request.geolocation,
            'status': validation_result['status'],
            'checks_performed': list(validation_result.get('checks', {}).keys()),
            'access_granted': validation_result['status'] == 'approved'
        }
        
        self.access_logs.append(log_entry)
    
    def detect_suspicious_activity(self) -> List[Dict]:
        """Detect suspicious access patterns and potential security threats"""
        suspicious_activities = []
        
        # Analyze recent access logs (last 24 hours)
        recent_logs = [log for log in self.access_logs 
                      if datetime.fromisoformat(log['timestamp']) > 
                      datetime.now() - timedelta(hours=24)]
        
        # Detect multiple failed access attempts
        user_failures = {}
        for log in recent_logs:
            if not log['access_granted']:
                user_id = log['user_id']
                user_failures[user_id] = user_failures.get(user_id, 0) + 1
        
        for user_id, failure_count in user_failures.items():
            if failure_count >= 5:  # Threshold for suspicious activity
                suspicious_activities.append({
                    'type': 'multiple_failed_attempts',
                    'user_id': user_id,
                    'failure_count': failure_count,
                    'severity': 'high' if failure_count >= 10 else 'medium',
                    'recommendation': 'Consider temporary account suspension'
                })
        
        # Detect unusual geographic access patterns
        user_locations = {}
        for log in recent_logs:
            if log['access_granted'] and log.get('geolocation'):
                user_id = log['user_id']
                country = log['geolocation'].get('country')
                if user_id not in user_locations:
                    user_locations[user_id] = set()
                user_locations[user_id].add(country)
        
        for user_id, countries in user_locations.items():
            if len(countries) > 3:  # Access from more than 3 countries
                suspicious_activities.append({
                    'type': 'unusual_geographic_pattern',
                    'user_id': user_id,
                    'countries': list(countries),
                    'severity': 'medium',
                    'recommendation': 'Verify user identity and recent travel'
                })
        
        return suspicious_activities
    
    def generate_security_report(self) -> Dict:
        """Generate comprehensive security report"""
        total_assets = len(self.assets)
        encrypted_assets = len([a for a in self.assets.values() 
                               if a['encryption_status'] == 'encrypted'])
        watermarked_assets = len([a for a in self.assets.values() 
                                 if a['watermark_status'] == 'applied'])
        
        recent_accesses = len([log for log in self.access_logs 
                              if datetime.fromisoformat(log['timestamp']) > 
                              datetime.now() - timedelta(hours=24)])
        
        successful_accesses = len([log for log in self.access_logs 
                                  if log['access_granted'] and 
                                  datetime.fromisoformat(log['timestamp']) > 
                                  datetime.now() - timedelta(hours=24)])
        
        suspicious_activities = self.detect_suspicious_activity()
        
        return {
            'report_generated': datetime.now().isoformat(),
            'asset_security': {
                'total_assets': total_assets,
                'encrypted_assets': encrypted_assets,
                'watermarked_assets': watermarked_assets,
                'encryption_coverage': f"{(encrypted_assets/total_assets)*100:.1f}%" if total_assets > 0 else "0%",
                'watermark_coverage': f"{(watermarked_assets/total_assets)*100:.1f}%" if total_assets > 0 else "0%"
            },
            'access_analytics': {
                'total_access_attempts_24h': recent_accesses,
                'successful_accesses_24h': successful_accesses,
                'success_rate': f"{(successful_accesses/recent_accesses)*100:.1f}%" if recent_accesses > 0 else "0%",
                'blocked_attempts': recent_accesses - successful_accesses
            },
            'threat_detection': {
                'suspicious_activities_detected': len(suspicious_activities),
                'high_severity_threats': len([a for a in suspicious_activities if a['severity'] == 'high']),
                'medium_severity_threats': len([a for a in suspicious_activities if a['severity'] == 'medium']),
                'activities': suspicious_activities
            },
            'security_posture': {
                'overall_score': self._calculate_security_score(),
                'recommendations': self._generate_security_recommendations()
            }
        }
    
    def _calculate_security_score(self) -> float:
        """Calculate overall security posture score (0-100)"""
        if not self.assets:
            return 0.0
        
        # Base score components
        encryption_score = len([a for a in self.assets.values() if a['encryption_status'] == 'encrypted']) / len(self.assets) * 40
        watermark_score = len([a for a in self.assets.values() if a['watermark_status'] == 'applied']) / len(self.assets) * 30
        
        # Access control effectiveness
        recent_logs = [log for log in self.access_logs 
                      if datetime.fromisoformat(log['timestamp']) > 
                      datetime.now() - timedelta(days=7)]
        
        if recent_logs:
            access_control_score = len([log for log in recent_logs if log['access_granted']]) / len(recent_logs) * 20
        else:
            access_control_score = 20  # Default if no recent activity
        
        # Threat detection effectiveness
        suspicious_activities = self.detect_suspicious_activity()
        threat_score = max(0, 10 - len(suspicious_activities))  # Penalty for unresolved threats
        
        return min(100, encryption_score + watermark_score + access_control_score + threat_score)
    
    def _generate_security_recommendations(self) -> List[str]:
        """Generate security recommendations based on current posture"""
        recommendations = []
        
        if not self.assets:
            return ['Register media assets to begin security monitoring']
        
        # Check encryption coverage
        encrypted_count = len([a for a in self.assets.values() if a['encryption_status'] == 'encrypted'])
        if encrypted_count / len(self.assets) < 0.8:
            recommendations.append('Increase encryption coverage to at least 80% of assets')
        
        # Check watermarking coverage
        watermarked_count = len([a for a in self.assets.values() if a['watermark_status'] == 'applied'])
        if watermarked_count / len(self.assets) < 0.6:
            recommendations.append('Implement watermarking for high-value content assets')
        
        # Check for suspicious activities
        suspicious_activities = self.detect_suspicious_activity()
        if suspicious_activities:
            recommendations.append(f'Investigate {len(suspicious_activities)} suspicious activities detected')
        
        # Check access patterns
        recent_failures = len([log for log in self.access_logs 
                              if not log['access_granted'] and 
                              datetime.fromisoformat(log['timestamp']) > 
                              datetime.now() - timedelta(hours=24)])
        
        if recent_failures > 10:
            recommendations.append('High number of access failures detected - review access policies')
        
        if not recommendations:
            recommendations.append('Security posture is strong - continue monitoring')
        
        return recommendations
    
    # Helper methods for geographic validation
    def _is_internal_ip(self, ip_address: str) -> bool:
        """Check if IP address is from internal network"""
        # Simplified check (in production, use proper IP range validation)
        internal_ranges = ['192.168.', '10.', '172.16.']
        return any(ip_address.startswith(range_) for range_ in internal_ranges)
    
    def _is_secure_facility(self, location: Dict) -> bool:
        """Check if location is a secure facility"""
        # Simplified check (in production, use geofencing with secure facility coordinates)
        secure_facilities = ['headquarters', 'data_center', 'secure_office']
        return location.get('facility_type') in secure_facilities

# Example usage:
# # Initialize security manager
# encryption_key = Fernet.generate_key()
# security_manager = MediaSecurityManager(encryption_key, "jwt-secret-key")
# 
# # Register a media asset
# asset = MediaAsset(
#     asset_id="video_001",
#     title="Confidential Training Video",
#     content_type=ContentType.VIDEO,
#     security_level=SecurityLevel.CONFIDENTIAL,
#     file_path="/secure/videos/training_001.mp4",
#     metadata={"duration": 1800, "resolution": "1080p"},
#     created_at=datetime.now(),
#     updated_at=datetime.now()
# )
# 
# registration_result = security_manager.register_media_asset(asset)
# print("Asset registered:", registration_result)
# 
# # Process access request
# access_request = AccessRequest(
#     user_id="user_123",
#     asset_id="video_001",
#     requested_permissions=["view", "download"],
#     request_time=datetime.now(),
#     ip_address="192.168.1.100",
#     user_agent="Mozilla/5.0...",
#     geolocation={"country": "US", "ip_address": "192.168.1.100"}
# )
# 
# access_result = security_manager.validate_access_request(access_request)
# print("Access validation:", access_result)
# 
# # Generate security report
# security_report = security_manager.generate_security_report()
# print("Security Report:", security_report)

class AIMediaSecuritySOC {
  constructor() {
    this.threatModels = new Map();
    this.incidents = [];
    this.users = new Map();
    this.assets = new Map();
    this.alertThresholds = {
      suspicious_login: { score: 0.7, action: 'monitor' },
      content_piracy: { score: 0.8, action: 'block' },
      data_exfiltration: { score: 0.9, action: 'isolate' },
      deepfake_detected: { score: 0.85, action: 'quarantine' },
      insider_threat: { score: 0.75, action: 'investigate' }
    };
    this.mlModels = this.initializeMLModels();
    this.responsePlaybooks = this.loadResponsePlaybooks();
  }

  initializeMLModels() {
    // Initialize various AI models for threat detection
    return {
      behaviorAnalysis: {
        name: 'User Behavior Analytics',
        type: 'anomaly_detection',
        confidence_threshold: 0.8,
        training_data_size: 100000,
        last_updated: new Date()
      },
      contentAuthenticity: {
        name: 'Deepfake Detection',
        type: 'neural_network',
        confidence_threshold: 0.85,
        supported_formats: ['video', 'audio', 'image'],
        last_updated: new Date()
      },
      networkTrafficAnalysis: {
        name: 'Network Anomaly Detection',
        type: 'ensemble',
        confidence_threshold: 0.75,
        features: ['packet_size', 'timing', 'protocols', 'endpoints'],
        last_updated: new Date()
      },
      threatIntelligence: {
        name: 'Threat Pattern Recognition',
        type: 'pattern_matching',
        confidence_threshold: 0.9,
        ioc_sources: ['commercial_feeds', 'government', 'industry_sharing'],
        last_updated: new Date()
      }
    };
  }

  loadResponsePlaybooks() {
    return {
      content_piracy: {
        immediate_actions: [
          'Block suspicious IP addresses',
          'Disable compromised user accounts',
          'Notify content protection team'
        ],
        investigation_steps: [
          'Analyze access patterns',
          'Identify source of leak',
          'Review user permissions',
          'Check for insider involvement'
        ],
        containment_measures: [
          'Revoke active sessions',
          'Update content watermarks',
          'Implement additional DRM controls'
        ]
      },
      insider_threat: {
        immediate_actions: [
          'Monitor user activity closely',
          'Backup critical data',
          'Alert security team discretely'
        ],
        investigation_steps: [
          'Review user access history',
          'Analyze behavioral changes',
          'Check for policy violations',
          'Interview supervisors'
        ],
        containment_measures: [
          'Limit system access',
          'Require supervisor approval',
          'Implement additional monitoring'
        ]
      },
      deepfake_attack: {
        immediate_actions: [
          'Quarantine suspicious content',
          'Alert content verification team',
          'Notify legal department'
        ],
        investigation_steps: [
          'Perform detailed content analysis',
          'Trace content origin',
          'Identify potential targets',
          'Assess reputational impact'
        ],
        containment_measures: [
          'Issue public correction',
          'Implement enhanced verification',
          'Update detection algorithms'
        ]
      }
    };
  }

  // Analyze user behavior for anomalies
  async analyzeUserBehavior(userId, activityData) {
    const userProfile = this.users.get(userId) || this.createUserProfile(userId);
    const behaviorScore = await this.calculateBehaviorAnomalyScore(userProfile, activityData);
    
    const analysis = {
      userId: userId,
      timestamp: new Date(),
      activityData: activityData,
      behaviorScore: behaviorScore,
      anomalies: [],
      riskLevel: 'low'
    };

    // Check for specific anomalies
    if (this.detectUnusualAccessPatterns(userProfile, activityData)) {
      analysis.anomalies.push({
        type: 'unusual_access_pattern',
        description: 'Access outside normal hours or locations',
        severity: 'medium',
        confidence: 0.8
      });
    }

    if (this.detectMassDataAccess(userProfile, activityData)) {
      analysis.anomalies.push({
        type: 'mass_data_access',
        description: 'Accessing unusually large amounts of content',
        severity: 'high',
        confidence: 0.9
      });
    }

    if (this.detectPrivilegeEscalation(userProfile, activityData)) {
      analysis.anomalies.push({
        type: 'privilege_escalation',
        description: 'Attempting to access unauthorized resources',
        severity: 'high',
        confidence: 0.85
      });
    }

    // Calculate overall risk level
    analysis.riskLevel = this.calculateRiskLevel(behaviorScore, analysis.anomalies);
    
    // Update user profile
    this.updateUserProfile(userId, activityData, analysis);
    
    // Generate alerts if necessary
    if (analysis.riskLevel !== 'low') {
      await this.generateSecurityAlert('behavioral_anomaly', analysis);
    }

    return analysis;
  }

  createUserProfile(userId) {
    const profile = {
      userId: userId,
      created: new Date(),
      lastSeen: new Date(),
      accessPatterns: {
        averageSessionDuration: 0,
        commonAccessTimes: [],
        commonLocations: [],
        typicalDataVolume: 0
      },
      behaviorBaseline: {
        loginFrequency: 0,
        contentAccessRate: 0,
        downloadVolume: 0,
        geographicConsistency: 1.0
      },
      riskHistory: [],
      permissions: [],
      department: 'unknown'
    };
    
    this.users.set(userId, profile);
    return profile;
  }

  async calculateBehaviorAnomalyScore(userProfile, activityData) {
    // Simulate ML model calculation
    let score = 0;
    const baseline = userProfile.behaviorBaseline;
    
    // Time-based anomaly
    const currentHour = new Date().getHours();
    const normalHours = userProfile.accessPatterns.commonAccessTimes;
    if (normalHours.length > 0 && !normalHours.includes(currentHour)) {
      score += 0.3;
    }
    
    // Volume-based anomaly
    const dataVolume = activityData.dataTransferred || 0;
    const typicalVolume = baseline.typicalDataVolume || 100;
    if (dataVolume > typicalVolume * 3) {
      score += 0.4;
    }
    
    // Location-based anomaly
    const currentLocation = activityData.location;
    const commonLocations = userProfile.accessPatterns.commonLocations;
    if (currentLocation && commonLocations.length > 0 && 
        !commonLocations.includes(currentLocation.country)) {
      score += 0.3;
    }
    
    // Normalize score to 0-1 range
    return Math.min(1.0, score);
  }

  detectUnusualAccessPatterns(userProfile, activityData) {
    const currentTime = new Date();
    const hour = currentTime.getHours();
    const dayOfWeek = currentTime.getDay();
    
    // Check if access is outside normal business hours
    const isWeekend = dayOfWeek === 0 || dayOfWeek === 6;
    const isAfterHours = hour < 6 || hour > 22;
    
    return (isWeekend || isAfterHours) && userProfile.department !== 'security';
  }

  detectMassDataAccess(userProfile, activityData) {
    const dataAccessed = activityData.assetsAccessed?.length || 0;
    const typicalAccess = userProfile.behaviorBaseline.contentAccessRate || 5;
    
    return dataAccessed > typicalAccess * 5;
  }

  detectPrivilegeEscalation(userProfile, activityData) {
    const requestedPermissions = activityData.permissionsRequested || [];
    const userPermissions = userProfile.permissions || [];
    
    return requestedPermissions.some(perm => !userPermissions.includes(perm));
  }

  calculateRiskLevel(behaviorScore, anomalies) {
    const anomalySeverity = anomalies.reduce((max, anomaly) => {
      const severityValues = { low: 1, medium: 2, high: 3 };
      return Math.max(max, severityValues[anomaly.severity] || 0);
    }, 0);
    
    if (behaviorScore >= 0.8 || anomalySeverity >= 3) {
      return 'high';
    } else if (behaviorScore >= 0.5 || anomalySeverity >= 2) {
      return 'medium';
    }
    return 'low';
  }

  // Detect deepfake content
  async analyzeContentAuthenticity(contentData) {
    const analysis = {
      contentId: contentData.contentId,
      contentType: contentData.type,
      timestamp: new Date(),
      authenticityScore: 0,
      deepfakeIndicators: [],
      confidence: 0,
      recommendation: 'unknown'
    };

    // Simulate deepfake detection based on content type
    switch (contentData.type) {
      case 'video':
        analysis.authenticityScore = await this.analyzeVideoAuthenticity(contentData);
        break;
      case 'audio':
        analysis.authenticityScore = await this.analyzeAudioAuthenticity(contentData);
        break;
      case 'image':
        analysis.authenticityScore = await this.analyzeImageAuthenticity(contentData);
        break;
      default:
        analysis.authenticityScore = 1.0; // Assume authentic for unknown types
    }

    // Determine recommendation
    if (analysis.authenticityScore < 0.3) {
      analysis.recommendation = 'likely_fake';
      analysis.confidence = 0.9;
    } else if (analysis.authenticityScore < 0.7) {
      analysis.recommendation = 'suspicious';
      analysis.confidence = 0.7;
    } else {
      analysis.recommendation = 'likely_authentic';
      analysis.confidence = 0.8;
    }

    // Generate alert for suspicious content
    if (analysis.recommendation !== 'likely_authentic') {
      await this.generateSecurityAlert('deepfake_detected', analysis);
    }

    return analysis;
  }

  async analyzeVideoAuthenticity(contentData) {
    // Simulate video deepfake detection
    const indicators = {
      facialInconsistencies: Math.random() < 0.2,
      temporalArtifacts: Math.random() < 0.15,
      compressionAnomalies: Math.random() < 0.25,
      lightingInconsistencies: Math.random() < 0.18
    };
    
    let score = 1.0;
    if (indicators.facialInconsistencies) score -= 0.4;
    if (indicators.temporalArtifacts) score -= 0.3;
    if (indicators.compressionAnomalies) score -= 0.2;
    if (indicators.lightingInconsistencies) score -= 0.25;
    
    return Math.max(0, score);
  }

  async analyzeAudioAuthenticity(contentData) {
    // Simulate audio deepfake detection
    const indicators = {
      spectralAnomalies: Math.random() < 0.3,
      voicePrintMismatch: Math.random() < 0.25,
      artificialArtifacts: Math.random() < 0.2
    };
    
    let score = 1.0;
    if (indicators.spectralAnomalies) score -= 0.4;
    if (indicators.voicePrintMismatch) score -= 0.5;
    if (indicators.artificialArtifacts) score -= 0.3;
    
    return Math.max(0, score);
  }

  async analyzeImageAuthenticity(contentData) {
    // Simulate image deepfake detection
    const indicators = {
      pixelInconsistencies: Math.random() < 0.2,
      metadataTampering: Math.random() < 0.15,
      compressionArtifacts: Math.random() < 0.25
    };
    
    let score = 1.0;
    if (indicators.pixelInconsistencies) score -= 0.3;
    if (indicators.metadataTampering) score -= 0.4;
    if (indicators.compressionArtifacts) score -= 0.2;
    
    return Math.max(0, score);
  }

  // Generate and manage security alerts
  async generateSecurityAlert(alertType, analysisData) {
    const alert = {
      id: `alert_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
      type: alertType,
      timestamp: new Date(),
      severity: this.calculateAlertSeverity(alertType, analysisData),
      status: 'open',
      data: analysisData,
      actions_taken: [],
      assigned_to: null,
      resolution_time: null
    };

    // Add to incidents log
    this.incidents.push(alert);

    // Trigger automated response based on severity
    await this.triggerAutomatedResponse(alert);

    // Notify security team
    await this.notifySecurityTeam(alert);

    console.log(`Security Alert Generated: ${alert.id} - ${alert.type} (${alert.severity})`);
    
    return alert;
  }

  calculateAlertSeverity(alertType, data) {
    const severityMatrix = {
      behavioral_anomaly: data.riskLevel === 'high' ? 'critical' : 
                         data.riskLevel === 'medium' ? 'high' : 'medium',
      deepfake_detected: data.recommendation === 'likely_fake' ? 'critical' : 'high',
      content_piracy: 'high',
      data_exfiltration: 'critical',
      insider_threat: 'high'
    };
    
    return severityMatrix[alertType] || 'medium';
  }

  async triggerAutomatedResponse(alert) {
    const playbook = this.responsePlaybooks[alert.type] || 
                    this.responsePlaybooks['default'];
    
    if (!playbook) return;

    // Execute immediate actions
    for (const action of playbook.immediate_actions || []) {
      try {
        await this.executeAction(action, alert);
        alert.actions_taken.push({
          action: action,
          timestamp: new Date(),
          status: 'completed',
          automated: true
        });
      } catch (error) {
        alert.actions_taken.push({
          action: action,
          timestamp: new Date(),
          status: 'failed',
          error: error.message,
          automated: true
        });
      }
    }
  }

  async executeAction(action, alert) {
    // Simulate various automated actions
    const actionHandlers = {
      'Block suspicious IP addresses': () => this.blockIPs(alert.data),
      'Disable compromised user accounts': () => this.disableUserAccounts(alert.data),
      'Quarantine suspicious content': () => this.quarantineContent(alert.data),
      'Monitor user activity closely': () => this.enhanceUserMonitoring(alert.data),
      'Revoke active sessions': () => this.revokeUserSessions(alert.data)
    };
    
    const handler = actionHandlers[action];
    if (handler) {
      await handler();
      console.log(`Automated action executed: ${action}`);
    }
  }

  async notifySecurityTeam(alert) {
    // Simulate notification to security team
    const notification = {
      to: 'security-team@company.com',
      subject: `Security Alert: ${alert.type} - ${alert.severity}`,
      body: `Alert ID: ${alert.id}\nType: ${alert.type}\nSeverity: ${alert.severity}\nTime: ${alert.timestamp}\n\nPlease review and investigate.`,
      priority: alert.severity === 'critical' ? 'high' : 'normal'
    };
    
    console.log('Security team notified:', notification.subject);
  }

  // Security dashboard and reporting
  generateSecurityDashboard() {
    const now = new Date();
    const last24h = new Date(now.getTime() - 24 * 60 * 60 * 1000);
    const last7d = new Date(now.getTime() - 7 * 24 * 60 * 60 * 1000);
    
    const recentIncidents = this.incidents.filter(i => i.timestamp >= last24h);
    const weeklyIncidents = this.incidents.filter(i => i.timestamp >= last7d);
    
    return {
      timestamp: now,
      summary: {
        total_incidents_24h: recentIncidents.length,
        total_incidents_7d: weeklyIncidents.length,
        critical_incidents_24h: recentIncidents.filter(i => i.severity === 'critical').length,
        open_incidents: this.incidents.filter(i => i.status === 'open').length
      },
      threat_breakdown: this.getIncidentBreakdown(recentIncidents),
      top_threats: this.getTopThreats(weeklyIncidents),
      user_risk_scores: this.getUserRiskScores(),
      system_health: {
        ml_models_status: this.getMLModelsStatus(),
        detection_accuracy: this.calculateDetectionAccuracy(),
        response_time_avg: this.calculateAverageResponseTime()
      },
      recommendations: this.generateSecurityRecommendations()
    };
  }

  getIncidentBreakdown(incidents) {
    const breakdown = {};
    incidents.forEach(incident => {
      breakdown[incident.type] = (breakdown[incident.type] || 0) + 1;
    });
    return breakdown;
  }

  getUserRiskScores() {
    const riskScores = [];
    this.users.forEach((profile, userId) => {
      const recentRisk = profile.riskHistory.slice(-5);
      const avgRisk = recentRisk.length > 0 ? 
        recentRisk.reduce((sum, risk) => sum + risk.score, 0) / recentRisk.length : 0;
      
      riskScores.push({
        userId: userId,
        riskScore: avgRisk,
        riskLevel: avgRisk >= 0.7 ? 'high' : avgRisk >= 0.4 ? 'medium' : 'low'
      });
    });
    
    return riskScores.sort((a, b) => b.riskScore - a.riskScore).slice(0, 10);
  }

  getMLModelsStatus() {
    const status = {};
    Object.entries(this.mlModels).forEach(([name, model]) => {
      const daysSinceUpdate = (new Date() - model.last_updated) / (1000 * 60 * 60 * 24);
      status[name] = {
        health: daysSinceUpdate < 7 ? 'healthy' : 'needs_update',
        confidence_threshold: model.confidence_threshold,
        last_updated: model.last_updated
      };
    });
    return status;
  }

  updateUserProfile(userId, activityData, analysis) {
    const profile = this.users.get(userId);
    if (!profile) return;
    
    profile.lastSeen = new Date();
    profile.riskHistory.push({
      timestamp: new Date(),
      score: analysis.behaviorScore,
      anomalies: analysis.anomalies.length
    });
    
    // Keep only last 30 risk entries
    if (profile.riskHistory.length > 30) {
      profile.riskHistory = profile.riskHistory.slice(-30);
    }
  }

  // Placeholder methods for automated actions
  async blockIPs(data) { console.log('Blocking suspicious IPs...'); }
  async disableUserAccounts(data) { console.log('Disabling user accounts...'); }
  async quarantineContent(data) { console.log('Quarantining suspicious content...'); }
  async enhanceUserMonitoring(data) { console.log('Enhancing user monitoring...'); }
  async revokeUserSessions(data) { console.log('Revoking user sessions...'); }
}

// Example usage:
// const soc = new AIMediaSecuritySOC();
// 
// // Analyze user behavior
// const behaviorAnalysis = await soc.analyzeUserBehavior('user123', {
//   assetsAccessed: ['video1', 'video2', 'video3'],
//   dataTransferred: 500, // MB
//   location: { country: 'US', city: 'New York' },
//   sessionDuration: 120 // minutes
// });
// 
// // Analyze content authenticity
// const authenticityAnalysis = await soc.analyzeContentAuthenticity({
//   contentId: 'content456',
//   type: 'video',
//   metadata: { duration: 180, resolution: '1080p' }
// });
// 
// // Generate security dashboard
// const dashboard = soc.generateSecurityDashboard();
// console.log('Security Dashboard:', dashboard);