IA et Cybersécurité pour PME : Protégez-vous Intelligemment

class CyberSecurityAI:
    def __init__(self):
        self.threat_detector = ThreatDetectionAI()
        self.behavior_analyzer = BehaviorAnalysisAI()
        self.vulnerability_scanner = VulnerabilityAssessmentAI()
        self.incident_responder = IncidentResponseAI()
        
    def implement_intelligent_security(self, network_infrastructure, business_profile):
        """
        Déploie protection cybersécurité complète pilotée par IA
        """
        # Détection menaces en temps réel
        threat_detection = self.threat_detector.setup_realtime_monitoring(
            network_infrastructure,
            detection_types=['malware', 'phishing', 'ransomware', 'insider_threats', 
                           'zero_day_exploits', 'social_engineering'],
            sensitivity_level='high',
            false_positive_tolerance='low'
        )
        
        # Analyse comportementale
        behavior_analysis = self.behavior_analyzer.analyze_network_behavior(
            network_infrastructure,
            baseline_establishment_days=30,
            anomaly_detection_threshold=0.85,
            user_behavior_profiling=True
        )
        
        # Assessment vulnérabilités
        vulnerability_assessment = self.vulnerability_scanner.continuous_vulnerability_scan(
            network_infrastructure,
            business_profile,
            scan_frequency='daily',
            priority_levels=['critical', 'high', 'medium', 'low']
        )
        
        # Système de réponse automatique
        incident_response = self.incident_responder.setup_automated_response(
            threat_detection,
            response_protocols=['isolate', 'block', 'alert', 'investigate', 'remediate'],
            escalation_rules=business_profile['security_policies']
        )
        
        return {
            'threat_protection': threat_detection,
            'behavioral_monitoring': behavior_analysis,
            'vulnerability_management': vulnerability_assessment,
            'incident_response': incident_response,
            'security_dashboard': self.create_security_dashboard(business_profile),
            'compliance_monitoring': self.setup_compliance_tracking(business_profile)
        }

class ThreatDetectionAI:
    def __init__(self):
        self.pattern_analyzer = NetworkPatternAnalyzer()
        self.anomaly_detector = AnomalyDetectionEngine()
        self.threat_classifier = ThreatClassificationAI()
        self.risk_assessor = RiskAssessmentAI()
        
    def detect_security_threats(self, network_traffic, user_activities, system_logs):
        """
        Détecte menaces cybersécurité avec analyse comportementale IA
        """
        # Analyse patterns réseau
        network_analysis = self.pattern_analyzer.analyze_network_patterns(
            network_traffic,
            baseline_period='30_days',
            pattern_types=['data_flow', 'connection_frequency', 'bandwidth_usage', 
                          'geographic_access', 'device_behavior']
        )
        
        # Détection anomalies comportementales
        behavioral_anomalies = self.anomaly_detector.detect_behavioral_anomalies(
            user_activities,
            normal_behavior_profile=network_analysis['user_baselines'],
            anomaly_types=['unusual_access_times', 'abnormal_data_access', 
                          'suspicious_file_operations', 'irregular_login_patterns']
        )
        
        # Classification des menaces
        threat_classification = self.threat_classifier.classify_potential_threats(
            behavioral_anomalies,
            threat_categories=['malware', 'insider_threat', 'external_breach', 
                             'social_engineering', 'data_exfiltration'],
            confidence_threshold=0.75
        )
        
        # Évaluation des risques
        risk_assessment = self.risk_assessor.assess_threat_risks(
            threat_classification,
            business_context=['data_sensitivity', 'system_criticality', 
                            'potential_impact', 'attack_sophistication'],
            risk_levels=['low', 'medium', 'high', 'critical']
        )
        
        return {
            'detected_threats': threat_classification,
            'risk_priorities': risk_assessment,
            'behavioral_insights': behavioral_anomalies,
            'network_intelligence': network_analysis,
            'automated_responses': self.generate_response_recommendations(risk_assessment),
            'investigation_guidance': self.create_investigation_playbooks(threat_classification)
        }

{
    "incident_analysis": {
        "threat_probability": 0.94,
        "threat_type": "insider_data_exfiltration",
        "severity_level": "critical",
        "confidence_score": 0.91,
        "behavioral_deviations": {
            "access_time": "extreme_anomaly",
            "data_volume": "critical_anomaly",
            "access_pattern": "systematic_vs_targeted",
            "geographic_anomaly": "moderate",
            "device_anomaly": "high"
        },
        "automated_actions_taken": [
            "immediate_session_termination",
            "account_temporary_suspension",
            "usb_device_access_blocked",
            "data_access_logged_and_flagged",
            "security_team_alert_sent",
            "legal_department_notification"
        ],
        "investigation_priorities": [
            "review_downloaded_files_content",
            "analyze_usb_device_forensics",
            "check_external_communications_recent",
            "verify_employee_status_changes",
            "assess_potential_data_compromise"
        ]
    }
}

class ZeroDayProtectionAI:
    def __init__(self):
        self.signature_analyzer = SignatureAnalysisAI()
        self.behavior_monitor = MalwareBehaviorMonitor()
        self.sandbox_analyzer = SandboxAnalysisAI()
        self.threat_intelligence = ThreatIntelligenceAI()
        
    def detect_unknown_threats(self, file_samples, network_traffic, system_behavior):
        """
        Détecte menaces zero-day sans signatures connues
        """
        # Analyse signatures dynamiques
        dynamic_signatures = self.signature_analyzer.analyze_dynamic_signatures(
            file_samples,
            analysis_techniques=['entropy_analysis', 'code_pattern_recognition',
                               'api_call_sequences', 'memory_usage_patterns']
        )
        
        # Monitoring comportement malware
        behavior_analysis = self.behavior_monitor.monitor_suspicious_behaviors(
            system_behavior,
            suspicious_activities=['file_encryption_mass', 'registry_modifications',
                                 'network_communications_unusual', 'privilege_escalation']
        )
        
        # Analyse sandbox
        sandbox_results = self.sandbox_analyzer.analyze_in_sandbox(
            file_samples,
            sandbox_environments=['windows_10', 'ubuntu_20', 'macos_12'],
            analysis_duration='5_minutes_deep_scan'
        )
        
        # Intelligence menaces
        threat_context = self.threat_intelligence.contextualize_threats(
            dynamic_signatures,
            behavior_analysis,
            threat_feeds=['commercial_feeds', 'open_source_intel', 'government_alerts']
        )
        
        return {
            'zero_day_probability': self.calculate_zero_day_probability(
                dynamic_signatures, behavior_analysis, sandbox_results
            ),
            'threat_sophistication': threat_context['sophistication_level'],
            'recommended_actions': self.generate_response_actions(threat_context),
            'containment_strategy': self.create_containment_plan(behavior_analysis)
        }

class AutomatedIncidentResponse:
    def __init__(self):
        self.incident_classifier = IncidentClassificationAI()
        self.response_orchestrator = ResponseOrchestrationAI()
        self.containment_system = ContainmentSystemAI()
        self.recovery_manager = RecoveryManagementAI()
        
    def execute_incident_response(self, security_incident, business_context):
        """
        Exécute réponse automatique incident sécurité
        """
        # Classification incident
        incident_classification = self.incident_classifier.classify_incident(
            security_incident,
            classification_criteria=['threat_type', 'severity_level', 'affected_systems',
                                   'data_sensitivity', 'business_impact_potential']
        )
        
        # Orchestration réponse
        response_plan = self.response_orchestrator.create_response_plan(
            incident_classification,
            business_context,
            response_objectives=['containment', 'eradication', 'recovery', 'lessons_learned']
        )
        
        # Containment automatique
        containment_actions = self.containment_system.execute_containment(
            security_incident,
            response_plan,
            containment_strategies=['network_isolation', 'account_suspension', 
                                   'system_quarantine', 'data_protection']
        )
        
        # Gestion recovery
        recovery_process = self.recovery_manager.manage_recovery_process(
            incident_classification,
            containment_actions,
            recovery_priorities=['business_continuity', 'data_integrity', 
                               'system_functionality', 'security_hardening']
        )
        
        return {
            'incident_response_summary': incident_classification,
            'automated_actions_taken': containment_actions,
            'recovery_status': recovery_process,
            'business_impact_assessment': self.assess_business_impact(
                incident_classification, containment_actions
            ),
            'lessons_learned': self.extract_lessons_learned(security_incident),
            'security_improvements': self.recommend_security_enhancements(incident_classification)
        }

class ComplianceAutomationAI:
    def __init__(self):
        self.regulation_monitor = RegulationMonitoringAI()
        self.compliance_assessor = ComplianceAssessmentAI()
        self.gap_analyzer = ComplianceGapAnalyzer()
        self.report_generator = ComplianceReportingAI()
        
    def manage_security_compliance(self, business_profile, regulatory_requirements):
        """
        Gère automatiquement conformité sécurité réglementaire
        """
        # Monitoring réglementations
        regulation_updates = self.regulation_monitor.monitor_regulation_changes(
            regulatory_requirements,
            jurisdictions=['canada_federal', 'quebec_provincial', 'industry_specific'],
            update_frequency='daily'
        )
        
        # Assessment conformité
        compliance_status = self.compliance_assessor.assess_compliance_status(
            business_profile,
            regulatory_requirements,
            assessment_areas=['data_protection', 'privacy_rights', 'security_controls',
                            'incident_reporting', 'audit_requirements']
        )
        
        # Analyse gaps conformité
        compliance_gaps = self.gap_analyzer.identify_compliance_gaps(
            compliance_status,
            regulation_updates,
            gap_priorities=['critical_non_compliance', 'medium_risk_gaps', 
                          'improvement_opportunities']
        )
        
        # Génération rapports
        compliance_reports = self.report_generator.generate_compliance_reports(
            compliance_status,
            compliance_gaps,
            report_types=['executive_summary', 'detailed_assessment', 
                         'action_plan', 'audit_trail']
        )
        
        return {
            'compliance_status': compliance_status,
            'identified_gaps': compliance_gaps,
            'automated_reports': compliance_reports,
            'remediation_plan': self.create_remediation_plan(compliance_gaps),
            'ongoing_monitoring': self.setup_continuous_compliance_monitoring()
        }

class CloudSecurityAI:
    def __init__(self):
        self.cloud_posture_manager = CloudPostureManagementAI()
        self.data_classifier = CloudDataClassificationAI()
        self.access_controller = IntelligentAccessController()
        self.workload_protector = WorkloadProtectionAI()
        
    def secure_cloud_environment(self, cloud_infrastructure, data_assets):
        """
        Sécurise environnement cloud avec intelligence IA
        """
        # Gestion posture cloud
        security_posture = self.cloud_posture_manager.assess_cloud_posture(
            cloud_infrastructure,
            security_frameworks=['CIS_benchmarks', 'NIST_cybersecurity', 'ISO_27001'],
            continuous_monitoring=True
        )
        
        # Classification données cloud
        data_classification = self.data_classifier.classify_cloud_data(
            data_assets,
            classification_levels=['public', 'internal', 'confidential', 'restricted'],
            automated_labeling=True
        )
        
        # Contrôle d'accès intelligent
        access_controls = self.access_controller.implement_intelligent_access(
            cloud_infrastructure,
            data_classification,
            access_policies=['zero_trust', 'least_privilege', 'just_in_time']
        )
        
        # Protection workloads
        workload_protection = self.workload_protector.protect_cloud_workloads(
            cloud_infrastructure,
            protection_types=['runtime_protection', 'vulnerability_management',
                            'compliance_monitoring', 'threat_detection']
        )
        
        return {
            'security_posture_score': security_posture,
            'data_protection_status': data_classification,
            'access_control_framework': access_controls,
            'workload_security': workload_protection,
            'cloud_compliance_status': self.assess_cloud_compliance(
                security_posture, data_classification
            )
        }

def calculate_cybersecurity_roi(business_profile):
    """
    Calcule ROI investissement cybersécurité IA
    """
    # Coûts cybersécurité IA
    monthly_security_cost = 3500  # CAD/mois stack intermédiaire
    annual_security_investment = monthly_security_cost * 12  # 42,000 CAD/an
    
    # Coûts évités (estimations conservatrices)
    costs_avoided = {
        'cyber_incident_prevention': {
            'probability_incident_without_ai': 0.67,  # 67% PME subissent incident
            'average_incident_cost': 156000,  # CAD coût moyen incident
            'ai_risk_reduction': 0.89,  # 89% réduction risque avec IA
            'annual_savings': 156000 * 0.67 * 0.89  # 93,012 CAD
        },
        'productivity_gains': {
            'it_staff_time_saved': 15,  # heures/semaine économisées
            'hourly_rate_it': 45,  # CAD/heure
            'annual_savings': 15 * 52 * 45  # 35,100 CAD
        },
        'compliance_automation': {
            'compliance_staff_reduction': 0.3,  # 30% réduction temps
            'compliance_staff_cost': 65000,  # CAD/an coût compliance
            'annual_savings': 65000 * 0.3  # 19,500 CAD
        },
        'business_continuity': {
            'downtime_hours_prevented': 48,  # heures downtime évitées/an
            'revenue_per_hour': 650,  # CAD revenus/heure business
            'annual_savings': 48 * 650  # 31,200 CAD
        }
    }
    
    total_annual_savings = sum([cost['annual_savings'] for cost in costs_avoided.values()])
    # Total : 178,812 CAD/an économies
    
    roi_calculation = {
        'annual_investment': annual_security_investment,
        'annual_savings': total_annual_savings,
        'net_annual_benefit': total_annual_savings - annual_security_investment,
        'roi_percentage': ((total_annual_savings - annual_security_investment) / annual_security_investment) * 100,
        'payback_period_months': (annual_security_investment / total_annual_savings) * 12
    }
    
    return roi_calculation

{
    "annual_investment": 42000,
    "annual_savings": 178812,
    "net_benefit": 136812,
    "roi_percentage": 326,
    "payback_period_months": 2.8
}