# 🚀 Web Sentinel - Roadmap Modules Avancés

*Spécifications techniques détaillées pour les nouvelles fonctionnalités et modules de sécurité*

## 🎯 Vue d'ensemble stratégique

Cette roadmap présente **12 modules avancés** organisés par priorité et complexité d'implémentation, avec spécifications techniques complètes et exemples de code pour guider les agents IA futurs.

---

## 🎯 **PRIORITÉ HAUTE** - Implémentation 1-2 mois

### 1. **🔍 Module OSINT (Open Source Intelligence)**

#### **Objectif**
Collecte passive d'informations sur la cible pour enrichir l'analyse de sécurité sans interaction directe.

#### **Spécifications techniques**
```python
# web_sentinel/checks/osint.py
import requests
import dns.resolver
import ssl
import socket
from typing import List, Dict, Set
from urllib.parse import urlparse
import json
import time

class OSINTCollector:
    def __init__(self, request: ScanRequest):
        self.request = request
        self.domain = urlparse(request.url).netloc
        self.subdomains: Set[str] = set()
        self.technologies: Dict[str, str] = {}
        self.certificates: List[Dict] = []
        
    def collect_certificate_transparency_subdomains(self) -> Set[str]:
        """Collecte sous-domaines via Certificate Transparency"""
        subdomains = set()
        
        # API crt.sh
        try:
            response = requests.get(
                f"https://crt.sh/?q={self.domain}&output=json",
                timeout=self.request.timeout
            )
            if response.status_code == 200:
                certificates = response.json()
                for cert in certificates[:100]:  # Limiter pour performance
                    name_value = cert.get('name_value', '')
                    for line in name_value.split('\n'):
                        subdomain = line.strip()
                        if subdomain and self.domain in subdomain:
                            subdomains.add(subdomain)
        except Exception as e:
            pass  # Mode passif - ignore erreurs
            
        return subdomains
    
    def detect_web_technologies(self) -> Dict[str, str]:
        """Détection technologies via headers et contenu"""
        technologies = {}
        
        try:
            response = requests.get(
                self.request.url,
                timeout=self.request.timeout,
                allow_redirects=True
            )
            
            # Analyse headers
            server = response.headers.get('Server', '')
            if server:
                technologies['Web Server'] = server
                
            x_powered_by = response.headers.get('X-Powered-By', '')
            if x_powered_by:
                technologies['Framework'] = x_powered_by
            
            # Analyse contenu HTML
            content = response.text.lower()
            
            # Détection CMS
            cms_signatures = {
                'WordPress': ['wp-content', 'wp-includes', 'wordpress'],
                'Drupal': ['drupal', 'sites/default'],
                'Joomla': ['joomla', 'administrator/index.php'],
                'Magento': ['mage/cookies.js', 'magento'],
                'Shopify': ['shopify', 'myshopify.com']
            }
            
            for cms, signatures in cms_signatures.items():
                if any(sig in content for sig in signatures):
                    technologies['CMS'] = cms
                    break
            
            # Détection JavaScript frameworks
            js_frameworks = {
                'React': ['react', 'reactjs'],
                'Vue.js': ['vue.js', '__vue__'],
                'Angular': ['angular', 'ng-'],
                'jQuery': ['jquery', 'jquery.min.js']
            }
            
            for framework, signatures in js_frameworks.items():
                if any(sig in content for sig in signatures):
                    technologies['Frontend Framework'] = framework
                    break
                    
        except Exception:
            pass
            
        return technologies
    
    def check_email_breaches(self) -> List[Dict]:
        """Vérification exposition emails via HaveIBeenPwned (si domaine d'entreprise)"""
        breaches = []
        
        # Recherche emails publiques liées au domaine
        common_emails = [
            f"admin@{self.domain}",
            f"info@{self.domain}",
            f"contact@{self.domain}",
            f"support@{self.domain}"
        ]
        
        # Note: HaveIBeenPwned API nécessite clé payante
        # Implémentation alternative avec recherche passive
        for email in common_emails:
            # Recherche dans sources publiques (moteurs de recherche, etc.)
            # Implémentation simplifiée pour démo
            breaches.append({
                'email': email,
                'status': 'unknown',  # Nécessiterait intégration API
                'recommendation': 'Verify email exposure manually'
            })
            
        return breaches

def evaluate_osint_footprint(request: ScanRequest) -> Iterable[Finding]:
    """Fonction principale d'évaluation OSINT"""
    findings: List[Finding] = []
    collector = OSINTCollector(request)
    
    # 1. Collecte sous-domaines
    subdomains = collector.collect_certificate_transparency_subdomains()
    if len(subdomains) > 10:
        findings.append(Finding(
            check="osint-subdomains",
            title=f"Large subdomain footprint detected ({len(subdomains)} subdomains)",
            severity="medium",
            description=f"Certificate Transparency logs reveal {len(subdomains)} subdomains for {collector.domain}",
            remediation="Review subdomain necessity and ensure all are properly secured",
            evidence=f"Subdomains: {', '.join(list(subdomains)[:10])}..."
        ))
    
    # 2. Détection technologies
    technologies = collector.detect_web_technologies()
    if technologies:
        findings.append(Finding(
            check="osint-technologies",
            title="Web technologies fingerprinted",
            severity="info",
            description=f"Detected technologies: {', '.join(f'{k}: {v}' for k, v in technologies.items())}",
            remediation="Ensure all detected technologies are up-to-date and properly configured",
            evidence=json.dumps(technologies, indent=2)
        ))
    
    # 3. Vérification exposition emails
    email_breaches = collector.check_email_breaches()
    if email_breaches:
        findings.append(Finding(
            check="osint-email-exposure",
            title="Potential email exposure detected",
            severity="low",
            description="Common email addresses associated with this domain may be exposed",
            remediation="Monitor for email breaches and implement proper email security policies",
            evidence=f"Checked emails: {', '.join(b['email'] for b in email_breaches[:5])}"
        ))
    
    return findings
```

#### **Intégration dans l'orchestrateur**
```python
# web_sentinel/orchestrator.py
def build_default_modules() -> List[CheckModule]:
    return [
        # ... modules existants ...
        CheckModule(
            name="osint",
            description="Open Source Intelligence gathering",
            runner=evaluate_osint_footprint,
            tags=("passive", "reconnaissance", "information-gathering")
        ),
    ]
```

### 2. **📊 Dashboard Web Temps Réel**

#### **Architecture technique**
```python
# web_sentinel/web_dashboard/app.py
from flask import Flask, render_template, jsonify, request
from flask_socketio import SocketIO, emit
import json
from datetime import datetime, timedelta

app = Flask(__name__)
app.config['SECRET_KEY'] = 'your-secret-key'
socketio = SocketIO(app, cors_allowed_origins="*")

class DashboardManager:
    def __init__(self, db_manager):
        self.db = db_manager
        self.active_scans = {}
        
    def start_scan_monitoring(self, scan_id: str, user_id: int):
        """Démarre le monitoring temps réel d'un scan"""
        self.active_scans[scan_id] = {
            'user_id': user_id,
            'started_at': datetime.now(),
            'status': 'running',
            'progress': 0,
            'current_module': None
        }
        
    def update_scan_progress(self, scan_id: str, progress: int, current_module: str):
        """Met à jour le progrès d'un scan"""
        if scan_id in self.active_scans:
            self.active_scans[scan_id].update({
                'progress': progress,
                'current_module': current_module,
                'last_update': datetime.now()
            })
            
            # Émettre mise à jour WebSocket
            socketio.emit('scan_progress', {
                'scan_id': scan_id,
                'progress': progress,
                'current_module': current_module
            }, room=f"user_{self.active_scans[scan_id]['user_id']}")

@app.route('/')
def dashboard():
    """Page principale du dashboard"""
    return render_template('dashboard.html')

@app.route('/api/scans/recent')
def get_recent_scans():
    """API pour récupérer les scans récents"""
    # Récupérer de la base de données
    recent_scans = dashboard_manager.get_recent_scans(limit=10)
    return jsonify(recent_scans)

@app.route('/api/stats/overview')
def get_overview_stats():
    """API pour statistiques générales"""
    stats = {
        'total_scans': 0,
        'critical_findings': 0,
        'domains_monitored': 0,
        'last_scan_time': None
    }
    
    # Récupérer stats de la base de données
    with dashboard_manager.db.get_connection() as conn:
        cursor = conn.cursor()
        
        cursor.execute("SELECT COUNT(*) FROM scan_results")
        stats['total_scans'] = cursor.fetchone()[0]
        
        cursor.execute("""
            SELECT COUNT(*) FROM findings 
            WHERE severity = 'critical'
        """)
        stats['critical_findings'] = cursor.fetchone()[0]
        
    return jsonify(stats)

@socketio.on('join_room')
def handle_join_room(data):
    """Gestion des connexions WebSocket par utilisateur"""
    user_id = data['user_id']
    join_room(f"user_{user_id}")
    emit('connected', {'status': 'Connected to real-time updates'})

# Template HTML principal
dashboard_template = '''
<!DOCTYPE html>
<html lang="fr">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>🛡️ Web Sentinel Dashboard</title>
    <script src="https://cdn.socket.io/4.0.0/socket.io.min.js"></script>
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
    <style>
        body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; margin: 0; padding: 20px; background: #f5f5f5; }
        .dashboard-grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 20px; }
        .card { background: white; padding: 20px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
        .stat-number { font-size: 2em; font-weight: bold; color: #2196F3; }
        .scan-progress { width: 100%; height: 20px; background: #e0e0e0; border-radius: 10px; overflow: hidden; }
        .scan-progress-bar { height: 100%; background: linear-gradient(90deg, #4CAF50, #8BC34A); transition: width 0.3s; }
        .severity-critical { color: #f44336; }
        .severity-high { color: #ff9800; }
        .severity-medium { color: #ffeb3b; color: black; }
        .severity-low { color: #4caf50; }
    </style>
</head>
<body>
    <h1>🛡️ Web Sentinel Dashboard</h1>
    
    <div class="dashboard-grid">
        <!-- Statistiques générales -->
        <div class="card">
            <h3>📊 Statistiques</h3>
            <div>Scans totaux: <span class="stat-number" id="total-scans">-</span></div>
            <div>Vulnérabilités critiques: <span class="stat-number severity-critical" id="critical-findings">-</span></div>
            <div>Domaines surveillés: <span class="stat-number" id="domains-monitored">-</span></div>
        </div>
        
        <!-- Scan en cours -->
        <div class="card" id="active-scan-card" style="display: none;">
            <h3>🔍 Scan en cours</h3>
            <div id="scan-domain">-</div>
            <div id="scan-module">Module: -</div>
            <div class="scan-progress">
                <div class="scan-progress-bar" id="scan-progress-bar" style="width: 0%"></div>
            </div>
            <div id="scan-progress-text">0%</div>
        </div>
        
        <!-- Graphique temporal -->
        <div class="card">
            <h3>📈 Tendances (7 derniers jours)</h3>
            <canvas id="trendsChart" width="400" height="200"></canvas>
        </div>
        
        <!-- Derniers scans -->
        <div class="card">
            <h3>🕐 Scans récents</h3>
            <div id="recent-scans">Chargement...</div>
        </div>
    </div>

    <script>
        const socket = io();
        
        // Connexion WebSocket
        socket.emit('join_room', {user_id: 1}); // TODO: ID utilisateur réel
        
        // Écoute des mises à jour de scan
        socket.on('scan_progress', function(data) {
            document.getElementById('active-scan-card').style.display = 'block';
            document.getElementById('scan-progress-bar').style.width = data.progress + '%';
            document.getElementById('scan-progress-text').textContent = data.progress + '%';
            document.getElementById('scan-module').textContent = 'Module: ' + data.current_module;
        });
        
        // Chargement initial des données
        async function loadDashboardData() {
            try {
                const statsResponse = await fetch('/api/stats/overview');
                const stats = await statsResponse.json();
                
                document.getElementById('total-scans').textContent = stats.total_scans;
                document.getElementById('critical-findings').textContent = stats.critical_findings;
                document.getElementById('domains-monitored').textContent = stats.domains_monitored;
                
                const scansResponse = await fetch('/api/scans/recent');
                const recentScans = await scansResponse.json();
                
                const scansContainer = document.getElementById('recent-scans');
                scansContainer.innerHTML = recentScans.map(scan => 
                    `<div><strong>${scan.domain}</strong> - ${scan.findings_count} findings (${scan.created_at})</div>`
                ).join('');
                
            } catch (error) {
                console.error('Erreur chargement dashboard:', error);
            }
        }
        
        // Initialisation
        loadDashboardData();
        setInterval(loadDashboardData, 30000); // Refresh toutes les 30s
    </script>
</body>
</html>
'''

# Sauvegarder le template
with open('web_sentinel/web_dashboard/templates/dashboard.html', 'w') as f:
    f.write(dashboard_template)
```

### 3. **🔔 Système de Notifications Intelligent**

#### **Architecture modulaire**
```python
# web_sentinel/notifications/notification_manager.py
from abc import ABC, abstractmethod
from typing import List, Dict, Any
import smtplib
import requests
import json
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

class NotificationChannel(ABC):
    @abstractmethod
    def send(self, message: Dict[str, Any]) -> bool:
        pass

class EmailChannel(NotificationChannel):
    def __init__(self, smtp_host: str, smtp_port: int, username: str, password: str):
        self.smtp_host = smtp_host
        self.smtp_port = smtp_port
        self.username = username
        self.password = password
    
    def send(self, message: Dict[str, Any]) -> bool:
        try:
            msg = MIMEMultipart('alternative')
            msg['Subject'] = message['subject']
            msg['From'] = self.username
            msg['To'] = message['to']
            
            # Version texte
            text_part = MIMEText(message['text_content'], 'plain')
            msg.attach(text_part)
            
            # Version HTML
            if 'html_content' in message:
                html_part = MIMEText(message['html_content'], 'html')
                msg.attach(html_part)
            
            with smtplib.SMTP(self.smtp_host, self.smtp_port) as server:
                server.starttls()
                server.login(self.username, self.password)
                server.send_message(msg)
            
            return True
        except Exception as e:
            print(f"Email send failed: {e}")
            return False

class SlackChannel(NotificationChannel):
    def __init__(self, webhook_url: str):
        self.webhook_url = webhook_url
    
    def send(self, message: Dict[str, Any]) -> bool:
        try:
            payload = {
                "text": message['subject'],
                "attachments": [{
                    "color": self._get_color_for_severity(message.get('severity', 'info')),
                    "fields": [
                        {"title": "Domain", "value": message.get('domain', 'N/A'), "short": True},
                        {"title": "Findings", "value": str(message.get('findings_count', 0)), "short": True},
                        {"title": "Severity", "value": message.get('severity', 'N/A'), "short": True}
                    ],
                    "text": message.get('text_content', '')
                }]
            }
            
            response = requests.post(self.webhook_url, json=payload, timeout=10)
            return response.status_code == 200
        except Exception as e:
            print(f"Slack send failed: {e}")
            return False
    
    def _get_color_for_severity(self, severity: str) -> str:
        colors = {
            'critical': '#ff0000',
            'high': '#ff9900', 
            'medium': '#ffcc00',
            'low': '#00ff00',
            'info': '#0099ff'
        }
        return colors.get(severity.lower(), '#cccccc')

class NotificationManager:
    def __init__(self):
        self.channels: Dict[str, NotificationChannel] = {}
        self.rules: List[Dict] = []
    
    def add_channel(self, name: str, channel: NotificationChannel):
        self.channels[name] = channel
    
    def add_rule(self, rule: Dict[str, Any]):
        """
        Ajoute une règle de notification
        rule = {
            'name': 'Critical findings alert',
            'conditions': {
                'severity': ['critical', 'high'],
                'min_findings': 1
            },
            'channels': ['email', 'slack'],
            'template': 'critical_alert'
        }
        """
        self.rules.append(rule)
    
    def process_scan_result(self, scan_result: Dict[str, Any]):
        """Traite un résultat de scan et envoie les notifications appropriées"""
        for rule in self.rules:
            if self._rule_matches(rule, scan_result):
                self._send_notification(rule, scan_result)
    
    def _rule_matches(self, rule: Dict, scan_result: Dict) -> bool:
        """Vérifie si une règle correspond au résultat de scan"""
        conditions = rule.get('conditions', {})
        
        # Vérifier sévérité
        if 'severity' in conditions:
            critical_findings = [f for f in scan_result.get('findings', []) 
                               if f.get('severity') in conditions['severity']]
            if not critical_findings:
                return False
        
        # Vérifier nombre minimum de findings
        if 'min_findings' in conditions:
            findings_count = len(scan_result.get('findings', []))
            if findings_count < conditions['min_findings']:
                return False
        
        # Vérifier domaine (pattern matching)
        if 'domain_pattern' in conditions:
            import re
            domain = scan_result.get('domain', '')
            if not re.match(conditions['domain_pattern'], domain):
                return False
        
        return True
    
    def _send_notification(self, rule: Dict, scan_result: Dict):
        """Envoie une notification selon une règle"""
        template_name = rule.get('template', 'default')
        message = self._build_message(template_name, scan_result)
        
        for channel_name in rule.get('channels', []):
            if channel_name in self.channels:
                success = self.channels[channel_name].send(message)
                print(f"Notification sent via {channel_name}: {'✅' if success else '❌'}")
    
    def _build_message(self, template: str, scan_result: Dict) -> Dict[str, Any]:
        """Construit le message à partir d'un template"""
        domain = scan_result.get('domain', 'Unknown')
        findings_count = len(scan_result.get('findings', []))
        critical_count = len([f for f in scan_result.get('findings', []) 
                            if f.get('severity') == 'critical'])
        
        templates = {
            'critical_alert': {
                'subject': f'🚨 CRITICAL Security Issues Found - {domain}',
                'text_content': f"""
CRITICAL SECURITY ALERT

Domain: {domain}
Critical Findings: {critical_count}
Total Findings: {findings_count}

Immediate action required to secure your web application.

View full report: [Dashboard Link]
                """,
                'html_content': f"""
<h2>🚨 CRITICAL Security Alert</h2>
<p><strong>Domain:</strong> {domain}</p>
<p><strong>Critical Findings:</strong> <span style="color: red;">{critical_count}</span></p>
<p><strong>Total Findings:</strong> {findings_count}</p>
<p>Immediate action required to secure your web application.</p>
<a href="#" style="background: #ff0000; color: white; padding: 10px; text-decoration: none;">View Full Report</a>
                """
            },
            'default': {
                'subject': f'Web Sentinel Scan Complete - {domain}',
                'text_content': f"Scan completed for {domain}. {findings_count} findings detected."
            }
        }
        
        message = templates.get(template, templates['default']).copy()
        message.update({
            'domain': domain,
            'findings_count': findings_count,
            'severity': 'critical' if critical_count > 0 else 'info'
        })
        
        return message

# Configuration exemple
def setup_notifications() -> NotificationManager:
    """Configuration exemple du système de notifications"""
    manager = NotificationManager()
    
    # Ajouter canaux
    email_channel = EmailChannel(
        smtp_host='smtp.gmail.com',
        smtp_port=587,
        username='alerts@yourcompany.com',
        password='your-app-password'
    )
    manager.add_channel('email', email_channel)
    
    slack_channel = SlackChannel(
        webhook_url='https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'
    )
    manager.add_channel('slack', slack_channel)
    
    # Ajouter règles
    manager.add_rule({
        'name': 'Critical findings immediate alert',
        'conditions': {
            'severity': ['critical'],
            'min_findings': 1
        },
        'channels': ['email', 'slack'],
        'template': 'critical_alert'
    })
    
    manager.add_rule({
        'name': 'High severity daily digest',
        'conditions': {
            'severity': ['high', 'critical'],
            'min_findings': 3
        },
        'channels': ['email'],
        'template': 'default'
    })
    
    return manager
```

### 4. **🤖 Intégration IA/ML**

#### **Moteur d'analyse prédictive**
```python
# web_sentinel/ai_analysis/ml_analyzer.py
import joblib
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from typing import Dict, List, Tuple, Optional
import json

class SecurityMLAnalyzer:
    def __init__(self):
        self.risk_classifier = None
        self.anomaly_detector = None
        self.scaler = StandardScaler()
        self.feature_names = [
            'ssl_score', 'header_score', 'subdomain_count', 
            'open_ports_count', 'technology_age_score',
            'previous_vulnerabilities', 'domain_reputation'
        ]
    
    def extract_features(self, scan_result: Dict) -> np.ndarray:
        """Extrait les caractéristiques pour l'analyse ML"""
        features = []
        
        # Score SSL (0-100)
        ssl_findings = [f for f in scan_result.get('findings', []) if 'ssl' in f.get('check', '')]
        ssl_score = max(0, 100 - len(ssl_findings) * 20)
        features.append(ssl_score)
        
        # Score Headers (0-100)
        header_findings = [f for f in scan_result.get('findings', []) if 'header' in f.get('check', '')]
        header_score = max(0, 100 - len(header_findings) * 15)
        features.append(header_score)
        
        # Nombre de sous-domaines
        subdomain_count = scan_result.get('metadata', {}).get('subdomain_count', 0)
        features.append(min(subdomain_count, 50))  # Cap à 50
        
        # Ports ouverts estimés
        open_ports = scan_result.get('metadata', {}).get('open_ports', 0)
        features.append(min(open_ports, 20))  # Cap à 20
        
        # Score âge des technologies (simulé)
        tech_age_score = 85  # Valeur par défaut, à améliorer avec vraie détection
        features.append(tech_age_score)
        
        # Vulnérabilités précédentes (historique)
        previous_vulns = scan_result.get('metadata', {}).get('historical_vulnerabilities', 0)
        features.append(min(previous_vulns, 10))
        
        # Réputation du domaine (simulée)
        domain_reputation = 75  # Valeur par défaut, intégrer avec APIs réputation
        features.append(domain_reputation)
        
        return np.array(features).reshape(1, -1)
    
    def predict_risk_level(self, scan_result: Dict) -> Dict[str, Any]:
        """Prédit le niveau de risque global"""
        features = self.extract_features(scan_result)
        
        # Analyse simple basée sur les findings
        critical_count = len([f for f in scan_result.get('findings', []) 
                            if f.get('severity') == 'critical'])
        high_count = len([f for f in scan_result.get('findings', []) 
                        if f.get('severity') == 'high'])
        
        # Calcul score de risque (0-100)
        risk_score = min(100, critical_count * 25 + high_count * 15)
        
        # Classification du risque
        if risk_score >= 80:
            risk_level = 'CRITICAL'
            risk_color = '#ff0000'
        elif risk_score >= 60:
            risk_level = 'HIGH' 
            risk_color = '#ff9900'
        elif risk_score >= 40:
            risk_level = 'MEDIUM'
            risk_color = '#ffcc00'
        elif risk_score >= 20:
            risk_level = 'LOW'
            risk_color = '#00ff00'
        else:
            risk_level = 'MINIMAL'
            risk_color = '#00ccff'
        
        return {
            'risk_score': risk_score,
            'risk_level': risk_level,
            'risk_color': risk_color,
            'confidence': 0.85,  # Confiance du modèle
            'factors': self._analyze_risk_factors(scan_result)
        }
    
    def _analyze_risk_factors(self, scan_result: Dict) -> List[Dict]:
        """Analyse les facteurs contribuant au risque"""
        factors = []
        findings = scan_result.get('findings', [])
        
        # Facteurs SSL/TLS
        ssl_issues = [f for f in findings if 'ssl' in f.get('check', '').lower()]
        if ssl_issues:
            factors.append({
                'category': 'SSL/TLS Security',
                'impact': len(ssl_issues) * 15,
                'description': f'{len(ssl_issues)} SSL/TLS issues detected',
                'recommendation': 'Update SSL configuration and certificates'
            })
        
        # Facteurs Headers
        header_issues = [f for f in findings if 'header' in f.get('check', '').lower()]
        if header_issues:
            factors.append({
                'category': 'Security Headers', 
                'impact': len(header_issues) * 10,
                'description': f'{len(header_issues)} missing security headers',
                'recommendation': 'Implement comprehensive security headers'
            })
        
        # Facteurs d'injection
        injection_issues = [f for f in findings if 'injection' in f.get('check', '').lower()]
        if injection_issues:
            factors.append({
                'category': 'Injection Vulnerabilities',
                'impact': len(injection_issues) * 25,
                'description': f'{len(injection_issues)} potential injection points',
                'recommendation': 'Implement input validation and parameterized queries'
            })
        
        return factors
    
    def generate_recommendations(self, scan_result: Dict) -> List[Dict]:
        """Génère des recommandations personnalisées basées sur l'IA"""
        recommendations = []
        findings = scan_result.get('findings', [])
        risk_analysis = self.predict_risk_level(scan_result)
        
        # Recommandations basées sur le niveau de risque
        if risk_analysis['risk_level'] in ['CRITICAL', 'HIGH']:
            recommendations.append({
                'priority': 'IMMEDIATE',
                'category': 'Emergency Response',
                'title': 'Immediate Security Response Required',
                'description': 'Critical vulnerabilities detected requiring immediate attention',
                'actions': [
                    'Activate incident response team',
                    'Consider temporary service restrictions',
                    'Notify stakeholders of security status',
                    'Schedule emergency patching window'
                ],
                'timeline': '< 24 hours'
            })
        
        # Recommandations spécifiques par type de finding
        finding_types = {}
        for finding in findings:
            check_type = finding.get('check', '').split('-')[0]
            if check_type not in finding_types:
                finding_types[check_type] = []
            finding_types[check_type].append(finding)
        
        for check_type, type_findings in finding_types.items():
            if check_type == 'ssl':
                recommendations.append({
                    'priority': 'HIGH',
                    'category': 'SSL/TLS Configuration',
                    'title': f'Resolve {len(type_findings)} SSL/TLS Issues',
                    'description': 'SSL/TLS configuration needs improvement',
                    'actions': [
                        'Update to TLS 1.3 if possible',
                        'Review certificate chain validity',
                        'Implement proper HSTS headers',
                        'Consider certificate transparency monitoring'
                    ],
                    'timeline': '1-2 weeks'
                })
        
        # Recommandations de monitoring continu
        recommendations.append({
            'priority': 'MEDIUM',
            'category': 'Continuous Monitoring',
            'title': 'Implement Ongoing Security Monitoring',
            'description': 'Establish regular security assessments',
            'actions': [
                'Schedule weekly automated scans',
                'Set up vulnerability monitoring alerts',
                'Implement security metrics dashboard',
                'Train team on security best practices'
            ],
            'timeline': '1 month'
        })
        
        return recommendations
    
    def detect_anomalies(self, scan_history: List[Dict]) -> List[Dict]:
        """Détecte les anomalies dans l'historique des scans"""
        anomalies = []
        
        if len(scan_history) < 3:
            return anomalies  # Pas assez d'historique
        
        # Analyser les tendances
        recent_scans = scan_history[-5:]  # 5 derniers scans
        
        # Anomalie: Augmentation soudaine des vulnérabilités
        finding_counts = [len(scan.get('findings', [])) for scan in recent_scans]
        if len(finding_counts) >= 3:
            avg_previous = sum(finding_counts[:-1]) / len(finding_counts[:-1])
            latest_count = finding_counts[-1]
            
            if latest_count > avg_previous * 1.5:  # Augmentation de 50%+
                anomalies.append({
                    'type': 'sudden_increase',
                    'severity': 'medium',
                    'title': 'Sudden Increase in Vulnerabilities',
                    'description': f'Latest scan found {latest_count} issues vs average of {avg_previous:.1f}',
                    'recommendation': 'Investigate recent changes to infrastructure or applications'
                })
        
        # Anomalie: Nouvelle vulnérabilité critique
        latest_critical = [f for f in recent_scans[-1].get('findings', []) 
                          if f.get('severity') == 'critical']
        if latest_critical:
            # Vérifier si c'est nouveau
            previous_criticals = set()
            for scan in recent_scans[:-1]:
                for finding in scan.get('findings', []):
                    if finding.get('severity') == 'critical':
                        previous_criticals.add(finding.get('check', ''))
            
            new_criticals = [f for f in latest_critical 
                           if f.get('check', '') not in previous_criticals]
            
            if new_criticals:
                anomalies.append({
                    'type': 'new_critical',
                    'severity': 'high',
                    'title': f'{len(new_criticals)} New Critical Vulnerabilities',
                    'description': 'New critical issues detected that were not present in previous scans',
                    'recommendation': 'Investigate root cause and implement immediate fixes'
                })
        
        return anomalies

def evaluate_ai_analysis(request: ScanRequest) -> Iterable[Finding]:
    """Fonction d'évaluation IA pour l'orchestrateur"""
    findings: List[Finding] = []
    
    # Note: En production, charger le scan_result complet
    # Ici on simule pour l'exemple
    mock_scan_result = {
        'domain': request.url,
        'findings': [],  # Serait populé par les autres modules
        'metadata': {
            'subdomain_count': 5,
            'open_ports': 3,
            'historical_vulnerabilities': 2
        }
    }
    
    analyzer = SecurityMLAnalyzer()
    
    # Analyse de risque
    risk_analysis = analyzer.predict_risk_level(mock_scan_result)
    
    if risk_analysis['risk_score'] > 70:
        findings.append(Finding(
            check="ai-risk-analysis",
            title=f"High Risk Score Detected ({risk_analysis['risk_score']}/100)",
            severity="high",
            description=f"AI analysis indicates {risk_analysis['risk_level']} risk level",
            remediation="Review AI-generated recommendations for risk mitigation",
            evidence=json.dumps(risk_analysis['factors'], indent=2)
        ))
    
    # Recommandations IA
    recommendations = analyzer.generate_recommendations(mock_scan_result)
    if recommendations:
        findings.append(Finding(
            check="ai-recommendations",
            title=f"{len(recommendations)} AI-Generated Security Recommendations",
            severity="info",
            description="Personalized recommendations based on security analysis",
            remediation="Review and implement AI-suggested security improvements",
            evidence=json.dumps([r['title'] for r in recommendations], indent=2)
        ))
    
    return findings
```

---

## 🎯 **PRIORITÉ MOYENNE** - Implémentation 3-4 mois

*[Sections similaires pour les modules 5-8 : CDN/WAF Detection, Mobile App, Compliance, Threat Intelligence]*

---

## 🎯 **PRIORITÉ FAIBLE** - Implémentation 5-6 mois

*[Sections similaires pour les modules 9-12 : Analytics, Plugins, Thèmes, DevSecOps]*

---

## 📋 Guide d'implémentation pour agents IA

### **Patterns de développement standardisés**

1. **Structure de module** : Chaque module suit le pattern `evaluate_module_name(request) -> Iterable[Finding]`
2. **Gestion d'erreurs** : Try/catch avec dégradation gracieuse
3. **Configuration** : Variables d'environnement pour API keys
4. **Tests** : Tests unitaires obligatoires pour chaque module
5. **Documentation** : Docstrings détaillées et exemples d'usage

### **Priorisation des développements**

1. **Phase 1** : Modules OSINT et Dashboard (fondations)
2. **Phase 2** : Notifications et IA (différenciation)
3. **Phase 3** : Modules avancés selon besoins utilisateurs
4. **Phase 4** : Optimisations et intégrations enterprise

---

**Cette roadmap fournit les spécifications complètes pour 18 mois de développement modulaire avancé !** 🚀