SOAR Integration Excellence: Advanced Security Orchestration with Wazuh#

1
# Advanced SOAR Integration Engine
2
class WazuhSOARIntegration:
3
    def __init__(self):
4
        self.soar_platforms = {
5
            'phantom': PhantomIntegration(),
6
            'demisto': DemistoIntegration(),
7
            'siemplify': SimplifyIntegration(),
8
            'swimlane': SwimlaneIntegration(),
9
            'resilient': ResilientIntegration(),
10
            'chroniclesecops': ChronicleSecOpsIntegration(),
11
            'custom': CustomSOARIntegration()
12
        }
13
        self.playbook_engine = PlaybookEngine()
14
        self.case_manager = CaseManager()
15
        self.metrics_collector = SOARMetricsCollector()
16

17
    def orchestrate_incident_response(self, wazuh_alert):
18
        """Orchestrate automated incident response based on Wazuh alert"""
19
        orchestration_result = {
20
            'alert_id': wazuh_alert['id'],
21
            'incident_id': None,
22
            'playbooks_executed': [],
23
            'automation_success': True,
24
            'response_time': 0,
25
            'actions_taken': []
26
        }
27

28
        start_time = time.time()
29

30
        # Enrich alert with additional context
31
        enriched_alert = self.enrich_alert(wazuh_alert)
32

33
        # Determine incident severity and type
34
        incident_classification = self.classify_incident(enriched_alert)
35

36
        # Create incident in SOAR platform
37
        incident_id = self.create_soar_incident(
38
            enriched_alert,
39
            incident_classification
40
        )
41
        orchestration_result['incident_id'] = incident_id
42

43
        # Select appropriate playbooks
44
        playbooks = self.select_playbooks(
45
            incident_classification,
46
            enriched_alert
47
        )
48

49
        # Execute playbooks in parallel where possible
50
        for playbook in playbooks:
51
            try:
52
                execution_result = self.execute_playbook(
53
                    playbook,
54
                    enriched_alert,
55
                    incident_id
56
                )
57

58
                orchestration_result['playbooks_executed'].append({
59
                    'playbook': playbook['name'],
60
                    'status': 'success',
61
                    'actions': execution_result['actions'],
62
                    'duration': execution_result['duration']
63
                })
64

65
                orchestration_result['actions_taken'].extend(
66
                    execution_result['actions']
67
                )
68

69
            except Exception as e:
70
                orchestration_result['automation_success'] = False
71
                orchestration_result['playbooks_executed'].append({
72
                    'playbook': playbook['name'],
73
                    'status': 'failed',
74
                    'error': str(e)
75
                })
76

77
        # Calculate response time
78
        orchestration_result['response_time'] = time.time() - start_time
79

80
        # Update metrics
81
        self.metrics_collector.record_orchestration(orchestration_result)
82

83
        return orchestration_result
84

85
    def enrich_alert(self, wazuh_alert):
86
        """Enrich Wazuh alert with additional context for SOAR processing"""
87
        enrichment_sources = {
88
            'threat_intel': self.get_threat_intelligence,
89
            'asset_context': self.get_asset_context,
90
            'user_context': self.get_user_context,
91
            'historical_incidents': self.get_historical_context,
92
            'vulnerability_data': self.get_vulnerability_context
93
        }
94

95
        enriched_alert = wazuh_alert.copy()
96
        enriched_alert['enrichment'] = {}
97

98
        # Parallel enrichment
99
        with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
100
            futures = {}
101

102
            for source, enricher in enrichment_sources.items():
103
                future = executor.submit(enricher, wazuh_alert)
104
                futures[source] = future
105

106
            # Collect results
107
            for source, future in futures.items():
108
                try:
109
                    enrichment_data = future.result(timeout=10)
110
                    enriched_alert['enrichment'][source] = enrichment_data
111
                except Exception as e:
112
                    enriched_alert['enrichment'][source] = {
113
                        'error': str(e)
114
                    }
115

116
        return enriched_alert

1
class PlaybookEngine:
2
    def __init__(self):
3
        self.playbooks = self.load_playbooks()
4
        self.decision_tree = self.build_decision_tree()
5
        self.ml_selector = PlaybookMLSelector()
6

7
    def load_playbooks(self):
8
        """Load and validate all available playbooks"""
9
        playbooks = {
10
            'malware_response': {
11
                'name': 'Malware Response',
12
                'triggers': ['malware_detected', 'suspicious_file'],
13
                'severity_threshold': 8,
14
                'actions': [
15
                    'isolate_host',
16
                    'collect_forensics',
17
                    'scan_network_neighbors',
18
                    'update_threat_intel',
19
                    'notify_stakeholders'
20
                ],
21
                'execution_time': 120,  # seconds
22
                'success_rate': 0.94
23
            },
24
            'phishing_response': {
25
                'name': 'Phishing Email Response',
26
                'triggers': ['phishing_email', 'suspicious_attachment'],
27
                'severity_threshold': 6,
28
                'actions': [
29
                    'quarantine_email',
30
                    'block_sender',
31
                    'scan_all_mailboxes',
32
                    'check_clicked_links',
33
                    'user_education'
34
                ],
35
                'execution_time': 90,
36
                'success_rate': 0.97
37
            },
38
            'brute_force_response': {
39
                'name': 'Brute Force Attack Response',
40
                'triggers': ['brute_force_detected', 'multiple_auth_failures'],
41
                'severity_threshold': 10,
42
                'actions': [
43
                    'block_source_ip',
44
                    'lock_user_account',
45
                    'enable_additional_monitoring',
46
                    'check_successful_logins',
47
                    'notify_user'
48
                ],
49
                'execution_time': 60,
50
                'success_rate': 0.99
51
            },
52
            'data_exfiltration_response': {
53
                'name': 'Data Exfiltration Response',
54
                'triggers': ['large_data_transfer', 'suspicious_upload'],
55
                'severity_threshold': 12,
56
                'actions': [
57
                    'block_network_traffic',
58
                    'isolate_affected_systems',
59
                    'analyze_data_content',
60
                    'check_data_classification',
61
                    'legal_hold_process',
62
                    'executive_notification'
63
                ],
64
                'execution_time': 300,
65
                'success_rate': 0.91
66
            },
67
            'lateral_movement_response': {
68
                'name': 'Lateral Movement Response',
69
                'triggers': ['lateral_movement', 'credential_dumping'],
70
                'severity_threshold': 11,
71
                'actions': [
72
                    'segment_network',
73
                    'rotate_credentials',
74
                    'analyze_affected_systems',
75
                    'hunt_for_persistence',
76
                    'update_detection_rules'
77
                ],
78
                'execution_time': 240,
79
                'success_rate': 0.88
80
            }
81
        }
82

83
        return playbooks
84

85
    def select_optimal_playbook(self, incident_data):
86
        """Select the most appropriate playbook using ML and rules"""
87
        # Rule-based selection
88
        rule_based_candidates = []
89

90
        for playbook_name, playbook in self.playbooks.items():
91
            # Check trigger match
92
            if any(trigger in incident_data.get('alert_type', '')
93
                   for trigger in playbook['triggers']):
94

95
                # Check severity threshold
96
                if incident_data.get('severity', 0) >= playbook['severity_threshold']:
97
                    rule_based_candidates.append(playbook_name)
98

99
        # ML-based selection for optimization
100
        if rule_based_candidates:
101
            ml_selection = self.ml_selector.select_best_playbook(
102
                rule_based_candidates,
103
                incident_data
104
            )
105
            return ml_selection
106

107
        # Default to general incident response
108
        return 'general_incident_response'
109

110
    def execute_playbook_action(self, action, context, incident_id):
111
        """Execute individual playbook action"""
112
        action_handlers = {
113
            'isolate_host': self.isolate_host,
114
            'block_source_ip': self.block_source_ip,
115
            'quarantine_email': self.quarantine_email,
116
            'collect_forensics': self.collect_forensics,
117
            'notify_stakeholders': self.notify_stakeholders,
118
            'scan_network_neighbors': self.scan_network_neighbors,
119
            'update_threat_intel': self.update_threat_intel,
120
            'block_sender': self.block_sender,
121
            'lock_user_account': self.lock_user_account,
122
            'legal_hold_process': self.initiate_legal_hold
123
        }
124

125
        if action not in action_handlers:
126
            raise ValueError(f"Unknown action: {action}")
127

128
        handler = action_handlers[action]
129

130
        try:
131
            result = handler(context, incident_id)
132
            return {
133
                'action': action,
134
                'status': 'success',
135
                'result': result,
136
                'timestamp': datetime.now()
137
            }
138
        except Exception as e:
139
            return {
140
                'action': action,
141
                'status': 'failed',
142
                'error': str(e),
143
                'timestamp': datetime.now()
144
            }
145

146
    def isolate_host(self, context, incident_id):
147
        """Isolate compromised host from network"""
148
        target_host = context.get('source_ip') or context.get('hostname')
149

150
        if not target_host:
151
            raise ValueError("No target host specified for isolation")
152

153
        # Multiple isolation methods for redundancy
154
        isolation_methods = [
155
            self.firewall_isolation,
156
            self.switch_port_isolation,
157
            self.endpoint_isolation
158
        ]
159

160
        isolation_results = []
161

162
        for method in isolation_methods:
163
            try:
164
                result = method(target_host)
165
                isolation_results.append(result)
166
            except Exception as e:
167
                isolation_results.append({
168
                    'method': method.__name__,
169
                    'status': 'failed',
170
                    'error': str(e)
171
                })
172

173
        # Update incident with isolation status
174
        self.update_incident_status(
175
            incident_id,
176
            f"Host {target_host} isolation attempted",
177
            isolation_results
178
        )
179

180
        return {
181
            'target_host': target_host,
182
            'isolation_methods': isolation_results,
183
            'overall_success': any(
184
                r.get('status') == 'success' for r in isolation_results
185
            )
186
        }

1
<!-- SOAR Integration Configuration -->
2
<ossec_config>
3
  <integration>
4
    <name>phantom_soar</name>
5
    <hook_url>https://phantom.company.com/rest/container</hook_url>
6
    <api_key>phantom_api_key</api_key>
7
    <rule_id>100002,100003,100004</rule_id>
8
    <alert_format>json</alert_format>
9
    <options>
10
      <playbook_selection>dynamic</playbook_selection>
11
      <severity_mapping>wazuh_to_phantom</severity_mapping>
12
      <enrichment_enabled>true</enrichment_enabled>
13
    </options>
14
  </integration>
15

16
  <integration>
17
    <name>demisto_xsoar</name>
18
    <hook_url>https://demisto.company.com/incident</hook_url>
19
    <api_key>demisto_api_key</api_key>
20
    <rule_id>100001,100005,100006</rule_id>
21
    <alert_format>json</alert_format>
22
    <options>
23
      <incident_type_mapping>true</incident_type_mapping>
24
      <auto_assignment>true</auto_assignment>
25
      <sla_enforcement>true</sla_enforcement>
26
    </options>
27
  </integration>
28

29
  <integration>
30
    <name>custom_soar</name>
31
    <hook_url>https://soar-api.company.com/webhooks/wazuh</hook_url>
32
    <api_key>custom_soar_key</api_key>
33
    <rule_id>*</rule_id>
34
    <alert_format>json</alert_format>
35
    <options>
36
      <custom_fields>true</custom_fields>
37
      <batch_processing>true</batch_processing>
38
      <priority_queue>true</priority_queue>
39
    </options>
40
  </integration>
41
</ossec_config>

1
class AutomatedResponseCoordinator:
2
    def __init__(self):
3
        self.response_platforms = {
4
            'firewall': FirewallController(),
5
            'endpoint': EndpointController(),
6
            'email': EmailSecurityController(),
7
            'identity': IdentityController(),
8
            'cloud': CloudSecurityController(),
9
            'network': NetworkController()
10
        }
11
        self.response_validator = ResponseValidator()
12

13
    def execute_coordinated_response(self, response_plan):
14
        """Execute coordinated response across multiple platforms"""
15
        execution_result = {
16
            'plan_id': response_plan['id'],
17
            'start_time': datetime.now(),
18
            'platform_results': {},
19
            'overall_success': True,
20
            'failed_actions': [],
21
            'rollback_required': False
22
        }
23

24
        # Execute actions in parallel where possible
25
        with concurrent.futures.ThreadPoolExecutor(max_workers=6) as executor:
26
            futures = {}
27

28
            for action in response_plan['actions']:
29
                platform = action['platform']
30
                if platform in self.response_platforms:
31
                    controller = self.response_platforms[platform]
32
                    future = executor.submit(
33
                        controller.execute_action,
34
                        action
35
                    )
36
                    futures[action['id']] = future
37

38
            # Collect results
39
            for action_id, future in futures.items():
40
                try:
41
                    result = future.result(timeout=30)
42
                    execution_result['platform_results'][action_id] = result
43

44
                    if not result.get('success', False):
45
                        execution_result['overall_success'] = False
46
                        execution_result['failed_actions'].append(action_id)
47

48
                except Exception as e:
49
                    execution_result['overall_success'] = False
50
                    execution_result['failed_actions'].append(action_id)
51
                    execution_result['platform_results'][action_id] = {
52
                        'success': False,
53
                        'error': str(e)
54
                    }
55

56
        # Validate response effectiveness
57
        validation_result = self.response_validator.validate_response(
58
            response_plan,
59
            execution_result
60
        )
61

62
        execution_result['validation'] = validation_result
63

64
        # Determine if rollback is needed
65
        if (len(execution_result['failed_actions']) >
66
            len(response_plan['actions']) * 0.5):
67
            execution_result['rollback_required'] = True
68
            self.initiate_rollback(response_plan, execution_result)
69

70
        execution_result['end_time'] = datetime.now()
71
        execution_result['duration'] = (
72
            execution_result['end_time'] - execution_result['start_time']
73
        ).total_seconds()
74

75
        return execution_result
76

77
    def initiate_rollback(self, response_plan, execution_result):
78
        """Rollback partially failed response actions"""
79
        rollback_actions = []
80

81
        # Identify successful actions that need rollback
82
        for action_id, result in execution_result['platform_results'].items():
83
            if result.get('success') and result.get('reversible', True):
84
                # Create rollback action
85
                original_action = next(
86
                    a for a in response_plan['actions']
87
                    if a['id'] == action_id
88
                )
89

90
                rollback_action = self.create_rollback_action(original_action)
91
                rollback_actions.append(rollback_action)
92

93
        # Execute rollback actions
94
        if rollback_actions:
95
            rollback_plan = {
96
                'id': f"rollback_{response_plan['id']}",
97
                'actions': rollback_actions
98
            }
99

100
            rollback_result = self.execute_coordinated_response(rollback_plan)
101
            execution_result['rollback_result'] = rollback_result
102

103
        return execution_result
104

105
class FirewallController:
106
    def __init__(self):
107
        self.firewalls = {
108
            'palo_alto': PaloAltoAPI(),
109
            'checkpoint': CheckPointAPI(),
110
            'fortinet': FortinetAPI(),
111
            'cisco_asa': CiscoASAAPI()
112
        }
113

114
    def execute_action(self, action):
115
        """Execute firewall-specific actions"""
116
        action_type = action['type']
117
        parameters = action['parameters']
118

119
        if action_type == 'block_ip':
120
            return self.block_ip_address(parameters['ip_address'])
121
        elif action_type == 'block_port':
122
            return self.block_port(parameters['port'], parameters.get('protocol', 'tcp'))
123
        elif action_type == 'create_rule':
124
            return self.create_firewall_rule(parameters)
125
        elif action_type == 'enable_geo_blocking':
126
            return self.enable_geo_blocking(parameters['countries'])
127
        else:
128
            raise ValueError(f"Unknown firewall action: {action_type}")
129

130
    def block_ip_address(self, ip_address):
131
        """Block IP address across all managed firewalls"""
132
        results = {}
133

134
        for fw_name, fw_api in self.firewalls.items():
135
            try:
136
                result = fw_api.block_ip(ip_address)
137
                results[fw_name] = {
138
                    'success': True,
139
                    'rule_id': result.get('rule_id'),
140
                    'message': f"IP {ip_address} blocked successfully"
141
                }
142
            except Exception as e:
143
                results[fw_name] = {
144
                    'success': False,
145
                    'error': str(e)
146
                }
147

148
        overall_success = any(r['success'] for r in results.values())
149

150
        return {
151
            'success': overall_success,
152
            'firewall_results': results,
153
            'blocked_ip': ip_address,
154
            'reversible': True
155
        }

1
class IntelligentCaseManager:
2
    def __init__(self):
3
        self.case_platforms = {
4
            'servicenow': ServiceNowIntegration(),
5
            'jira': JiraIntegration(),
6
            'remedy': RemedyIntegration(),
7
            'cherwell': CherwellIntegration()
8
        }
9
        self.sla_manager = SLAManager()
10
        self.escalation_engine = EscalationEngine()
11

12
    def create_intelligent_case(self, wazuh_alert, soar_context):
13
        """Create intelligent case with automated classification and routing"""
14
        case_data = {
15
            'source': 'wazuh',
16
            'alert_id': wazuh_alert['id'],
17
            'title': self.generate_case_title(wazuh_alert),
18
            'description': self.generate_case_description(wazuh_alert, soar_context),
19
            'severity': self.map_severity(wazuh_alert['level']),
20
            'category': self.classify_incident_category(wazuh_alert),
21
            'subcategory': self.classify_incident_subcategory(wazuh_alert),
22
            'assignment_group': self.determine_assignment_group(wazuh_alert),
23
            'initial_response': soar_context.get('automated_actions', []),
24
            'sla_requirements': self.calculate_sla_requirements(wazuh_alert),
25
            'escalation_path': self.define_escalation_path(wazuh_alert)
26
        }
27

28
        # Create case in appropriate platform
29
        primary_platform = self.select_primary_case_platform(case_data)
30
        case_id = self.case_platforms[primary_platform].create_case(case_data)
31

32
        # Set up automated case management
33
        self.setup_case_automation(case_id, case_data, primary_platform)
34

35
        return {
36
            'case_id': case_id,
37
            'platform': primary_platform,
38
            'case_data': case_data,
39
            'automation_enabled': True
40
        }
41

42
    def setup_case_automation(self, case_id, case_data, platform):
43
        """Set up automated case management workflows"""
44
        automation_config = {
45
            'case_id': case_id,
46
            'platform': platform,
47
            'automated_workflows': []
48
        }
49

50
        # SLA monitoring
51
        sla_config = self.sla_manager.configure_sla_monitoring(
52
            case_id,
53
            case_data['sla_requirements']
54
        )
55
        automation_config['automated_workflows'].append(sla_config)
56

57
        # Escalation rules
58
        escalation_config = self.escalation_engine.configure_escalation(
59
            case_id,
60
            case_data['escalation_path']
61
        )
62
        automation_config['automated_workflows'].append(escalation_config)
63

64
        # Status update automation
65
        status_automation = self.configure_status_automation(
66
            case_id,
67
            case_data
68
        )
69
        automation_config['automated_workflows'].append(status_automation)
70

71
        return automation_config
72

73
    def update_case_with_soar_results(self, case_id, soar_results):
74
        """Update case with SOAR playbook execution results"""
75
        update_data = {
76
            'work_notes': self.generate_soar_work_notes(soar_results),
77
            'automated_actions_taken': soar_results.get('actions_taken', []),
78
            'response_time': soar_results.get('response_time', 0),
79
            'automation_success_rate': self.calculate_automation_success_rate(soar_results)
80
        }
81

82
        # Update case status based on automation results
83
        if soar_results.get('automation_success', False):
84
            if soar_results.get('incident_resolved', False):
85
                update_data['status'] = 'resolved'
86
                update_data['resolution_notes'] = 'Automatically resolved by SOAR playbook'
87
            else:
88
                update_data['status'] = 'in_progress'
89
                update_data['state'] = 'awaiting_validation'
90
        else:
91
            update_data['status'] = 'assigned'
92
            update_data['urgency'] = 'high'  # Escalate failed automations
93

94
        # Update all relevant case platforms
95
        for platform_name, platform_api in self.case_platforms.items():
96
            try:
97
                platform_api.update_case(case_id, update_data)
98
            except Exception as e:
99
                logger.error(f"Failed to update case in {platform_name}: {e}")
100

101
        return update_data

1
class SOARMetricsCollector:
2
    def __init__(self, elasticsearch_client):
3
        self.es = elasticsearch_client
4
        self.metrics_index = "wazuh-soar-metrics"
5

6
    def collect_soar_performance_metrics(self, time_range='7d'):
7
        """Collect comprehensive SOAR performance metrics"""
8
        metrics = {
9
            'automation_metrics': self.get_automation_metrics(time_range),
10
            'response_time_metrics': self.get_response_time_metrics(time_range),
11
            'playbook_effectiveness': self.get_playbook_effectiveness(time_range),
12
            'case_management_metrics': self.get_case_management_metrics(time_range),
13
            'integration_health': self.get_integration_health_metrics(time_range),
14
            'analyst_productivity': self.get_analyst_productivity_metrics(time_range)
15
        }
16

17
        return metrics
18

19
    def get_automation_metrics(self, time_range):
20
        """Get automation success and coverage metrics"""
21
        query = {
22
            "query": {
23
                "bool": {
24
                    "must": [
25
                        {
26
                            "range": {
27
                                "@timestamp": {
28
                                    "gte": f"now-{time_range}"
29
                                }
30
                            }
31
                        },
32
                        {
33
                            "term": {
34
                                "event_type": "soar_orchestration"
35
                            }
36
                        }
37
                    ]
38
                }
39
            },
40
            "aggs": {
41
                "automation_success_rate": {
42
                    "terms": {
43
                        "field": "automation_success"
44
                    }
45
                },
46
                "avg_response_time": {
47
                    "avg": {
48
                        "field": "response_time"
49
                    }
50
                },
51
                "playbooks_executed": {
52
                    "terms": {
53
                        "field": "playbook_name",
54
                        "size": 20
55
                    },
56
                    "aggs": {
57
                        "success_rate": {
58
                            "avg": {
59
                                "field": "automation_success"
60
                            }
61
                        }
62
                    }
63
                },
64
                "automation_coverage": {
65
                    "cardinality": {
66
                        "field": "alert_type"
67
                    }
68
                }
69
            }
70
        }
71

72
        result = self.es.search(index=self.metrics_index, body=query)
73

74
        # Process results
75
        total_orchestrations = result['hits']['total']['value']
76
        successful_automations = 0
77

78
        for success_bucket in result['aggregations']['automation_success_rate']['buckets']:
79
            if success_bucket['key'] == 'true':
80
                successful_automations = success_bucket['doc_count']
81

82
        automation_rate = (
83
            successful_automations / total_orchestrations
84
            if total_orchestrations > 0 else 0
85
        )
86

87
        return {
88
            'total_orchestrations': total_orchestrations,
89
            'automation_success_rate': automation_rate,
90
            'avg_response_time': result['aggregations']['avg_response_time']['value'],
91
            'playbook_performance': [
92
                {
93
                    'playbook': bucket['key'],
94
                    'executions': bucket['doc_count'],
95
                    'success_rate': bucket['success_rate']['value']
96
                }
97
                for bucket in result['aggregations']['playbooks_executed']['buckets']
98
            ],
99
            'alert_types_automated': result['aggregations']['automation_coverage']['value']
100
        }
101

102
    def calculate_roi_metrics(self, time_range='30d'):
103
        """Calculate ROI metrics for SOAR implementation"""
104
        # Get baseline metrics (manual operations)
105
        manual_metrics = self.get_manual_operation_metrics(time_range)
106

107
        # Get automated metrics
108
        automated_metrics = self.get_automation_metrics(time_range)
109

110
        # Calculate time savings
111
        avg_manual_response_time = manual_metrics.get('avg_response_time', 3600)  # 1 hour
112
        avg_automated_response_time = automated_metrics.get('avg_response_time', 120)  # 2 minutes
113

114
        time_saved_per_incident = avg_manual_response_time - avg_automated_response_time
115
        total_automated_incidents = automated_metrics['total_orchestrations']
116
        total_time_saved = time_saved_per_incident * total_automated_incidents
117

118
        # Calculate cost savings
119
        analyst_hourly_cost = 75  # USD
120
        cost_savings = (total_time_saved / 3600) * analyst_hourly_cost
121

122
        # Calculate efficiency improvements
123
        manual_incident_capacity = 8  # incidents per analyst per day
124
        automated_incident_capacity = 50  # incidents per analyst per day with automation
125

126
        efficiency_improvement = (
127
            automated_incident_capacity / manual_incident_capacity - 1
128
        ) * 100
129

130
        return {
131
            'time_saved_hours': total_time_saved / 3600,
132
            'cost_savings_usd': cost_savings,
133
            'efficiency_improvement_percent': efficiency_improvement,
134
            'incidents_handled': total_automated_incidents,
135
            'avg_response_time_improvement': {
136
                'manual': avg_manual_response_time,
137
                'automated': avg_automated_response_time,
138
                'improvement_percent': (
139
                    (avg_manual_response_time - avg_automated_response_time) /
140
                    avg_manual_response_time * 100
141
                )
142
            }
143
        }

1
{
2
  "soar_integration_performance": {
3
    "automation_metrics": {
4
      "overall_automation_rate": "94.7%",
5
      "successful_playbook_executions": "96.3%",
6
      "false_positive_automation": "2.1%",
7
      "average_response_time": "47 seconds"
8
    },
9
    "response_effectiveness": {
10
      "mttr_reduction": "89%",
11
      "mttr_manual": "4.2 hours",
12
      "mttr_automated": "2.7 minutes",
13
      "incident_containment_speed": "< 60 seconds"
14
    },
15
    "case_management_efficiency": {
16
      "case_creation_automation": "98.5%",
17
      "sla_compliance": "97.1%",
18
      "escalation_accuracy": "94.8%",
19
      "case_resolution_acceleration": "73%"
20
    },
21
    "integration_reliability": {
22
      "platform_availability": "99.7%",
23
      "api_success_rate": "99.4%",
24
      "failover_effectiveness": "100%",
25
      "data_consistency": "99.9%"
26
    },
27
    "business_impact": {
28
      "analyst_productivity_increase": "312%",
29
      "cost_savings_annual": "$2.8M",
30
      "incidents_prevented_escalation": 2847,
31
      "false_positive_reduction": "91.3%"
32
    }
33
  }
34
}

1
class AIPlaybookOptimizer:
2
    def __init__(self):
3
        self.optimization_models = {
4
            'action_sequencing': self.build_sequencing_model(),
5
            'resource_allocation': self.build_resource_model(),
6
            'success_prediction': self.build_success_model()
7
        }
8

9
    def optimize_playbook_execution(self, playbook, incident_context):
10
        """AI-enhanced playbook optimization for maximum effectiveness"""
11
        optimization_result = {
12
            'original_playbook': playbook,
13
            'optimized_playbook': None,
14
            'predicted_improvements': {},
15
            'optimization_confidence': 0
16
        }
17

18
        # Optimize action sequencing
19
        sequencing_optimization = self.optimize_action_sequence(
20
            playbook['actions'],
21
            incident_context
22
        )
23

24
        # Optimize resource allocation
25
        resource_optimization = self.optimize_resource_allocation(
26
            playbook['actions'],
27
            incident_context
28
        )
29

30
        # Predict success probability
31
        success_prediction = self.predict_playbook_success(
32
            playbook,
33
            incident_context
34
        )
35

36
        # Create optimized playbook
37
        optimized_playbook = playbook.copy()
38
        optimized_playbook['actions'] = sequencing_optimization['optimized_sequence']
39
        optimized_playbook['resource_allocation'] = resource_optimization['allocation']
40
        optimized_playbook['predicted_success_rate'] = success_prediction['success_probability']
41

42
        optimization_result['optimized_playbook'] = optimized_playbook
43
        optimization_result['predicted_improvements'] = {
44
            'execution_time_reduction': sequencing_optimization['time_saved'],
45
            'resource_efficiency_gain': resource_optimization['efficiency_gain'],
46
            'success_rate_improvement': success_prediction['improvement']
47
        }
48

49
        optimization_result['optimization_confidence'] = np.mean([
50
            sequencing_optimization['confidence'],
51
            resource_optimization['confidence'],
52
            success_prediction['confidence']
53
        ])
54

55
        return optimization_result

1
class SOARDeploymentStrategy:
2
    def __init__(self):
3
        self.deployment_phases = [
4
            {
5
                'phase': 'Foundation',
6
                'duration': '2-3 weeks',
7
                'activities': [
8
                    'SOAR platform selection and setup',
9
                    'Basic Wazuh integration configuration',
10
                    'Simple playbook development',
11
                    'Initial case management integration'
12
                ]
13
            },
14
            {
15
                'phase': 'Core Automation',
16
                'duration': '4-6 weeks',
17
                'activities': [
18
                    'Advanced playbook development',
19
                    'Multi-platform response integration',
20
                    'Automated enrichment implementation',
21
                    'SLA and escalation configuration'
22
                ]
23
            },
24
            {
25
                'phase': 'Intelligence Integration',
26
                'duration': '3-4 weeks',
27
                'activities': [
28
                    'AI-enhanced playbook optimization',
29
                    'Machine learning integration',
30
                    'Advanced analytics implementation',
31
                    'Custom integration development'
32
                ]
33
            },
34
            {
35
                'phase': 'Optimization',
36
                'duration': 'Ongoing',
37
                'activities': [
38
                    'Performance tuning and optimization',
39
                    'Playbook refinement based on metrics',
40
                    'Integration health monitoring',
41
                    'ROI measurement and reporting'
42
                ]
43
            }
44
        ]