Advanced Incident Response Automation: From Detection to Remediation in Cloud-Native Environments

The Modern Incident Response Challenge

Your cloud-native environment generates millions of security events daily across containers, serverless functions, microservices, and multi-cloud infrastructure. Security teams struggle to investigate alerts manually, often taking hours to determine if an event is a false positive or a legitimate threat. Meanwhile, sophisticated attackers move laterally through your environment in minutes, not hours. This speed differential between detection and response creates an impossible gap that traditional manual processes cannot close.

Advanced incident response automation bridges this gap by implementing intelligent detection, automated analysis, and orchestrated remediation that operates at machine speed while maintaining human oversight for critical decisions.

Cloud-Native Incident Response Architecture

Modern incident response requires a fundamentally different approach than traditional on-premises security. Cloud-native environments demand automation that can operate across ephemeral infrastructure, distributed services, and dynamic scaling scenarios while maintaining comprehensive audit trails and forensic capabilities.

Core Components of Automated Incident Response

1. Intelligent Detection Layer

• Multi-source event correlation across cloud services
• Machine learning-based anomaly detection
• Behavioral analysis for insider threat detection
• Integration with threat intelligence feeds

2. Automated Analysis Engine

• Event triage and severity classification
• Automated threat hunting and context enrichment
• Dynamic forensic data collection
• Evidence preservation and chain of custody

3. Orchestrated Response Platform

• Automated containment and isolation
• Dynamic playbook execution
• Stakeholder notification and communication
• Recovery and remediation workflows

4. Continuous Learning System

• Response effectiveness analysis
• Playbook optimization and tuning
• Threat intelligence enrichment
• Skills and knowledge base updates

SOAR Implementation for Cloud Environments

1. Advanced Security Orchestration Platform

# incident-response/soar_platform.py
from typing import Dict, List, Optional, Any, Callable
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from enum import Enum
import asyncio
import json
import uuid
import logging
from concurrent.futures import ThreadPoolExecutor
import boto3
import requests

class IncidentSeverity(str, Enum):
    CRITICAL = "critical"
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"
    INFO = "info"

class IncidentStatus(str, Enum):
    OPEN = "open"
    INVESTIGATING = "investigating"
    CONTAINED = "contained"
    RESOLVING = "resolving"
    RESOLVED = "resolved"
    CLOSED = "closed"

class ResponseAction(str, Enum):
    INVESTIGATE = "investigate"
    CONTAIN = "contain"
    ISOLATE = "isolate"
    REMEDIATE = "remediate"
    NOTIFY = "notify"
    ESCALATE = "escalate"

@dataclass
class SecurityEvent:
    event_id: str
    timestamp: datetime
    source: str
    event_type: str
    severity: IncidentSeverity
    raw_data: Dict[str, Any]
    indicators: List[str] = field(default_factory=list)
    context: Dict[str, Any] = field(default_factory=dict)
    confidence_score: float = 0.0

@dataclass
class IncidentCase:
    incident_id: str
    title: str
    description: str
    severity: IncidentSeverity
    status: IncidentStatus
    created_at: datetime
    updated_at: datetime
    events: List[SecurityEvent] = field(default_factory=list)
    evidence: List[Dict] = field(default_factory=list)
    actions_taken: List[Dict] = field(default_factory=list)
    assigned_to: Optional[str] = None
    playbook_id: Optional[str] = None
    tags: List[str] = field(default_factory=list)

class ThreatIntelligenceProvider:
    """Integration with threat intelligence feeds"""

    def __init__(self):
        self.providers = {
            'mitre_attack': self._query_mitre_attack,
            'virustotal': self._query_virustotal,
            'alienvault': self._query_alienvault,
            'custom_feeds': self._query_custom_feeds
        }

        self.ioc_cache = {}
        self.cache_expiry = timedelta(hours=6)

    async def enrich_indicators(self, indicators: List[str]) -> Dict[str, Any]:
        """Enrich indicators with threat intelligence"""

        enrichment_results = {
            'indicators': {},
            'tactics': [],
            'techniques': [],
            'threat_actors': [],
            'campaigns': [],
            'confidence_score': 0.0
        }

        # Process each indicator
        for indicator in indicators:
            indicator_intel = await self._get_indicator_intelligence(indicator)
            enrichment_results['indicators'][indicator] = indicator_intel

            # Aggregate threat intelligence
            if indicator_intel.get('tactics'):
                enrichment_results['tactics'].extend(indicator_intel['tactics'])
            if indicator_intel.get('techniques'):
                enrichment_results['techniques'].extend(indicator_intel['techniques'])
            if indicator_intel.get('threat_actors'):
                enrichment_results['threat_actors'].extend(indicator_intel['threat_actors'])

        # Calculate overall confidence score
        confidence_scores = [
            intel.get('confidence', 0)
            for intel in enrichment_results['indicators'].values()
        ]
        enrichment_results['confidence_score'] = sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0

        return enrichment_results

    async def _get_indicator_intelligence(self, indicator: str) -> Dict:
        """Get intelligence for a specific indicator"""

        # Check cache first
        cache_key = f"intel_{indicator}"
        if cache_key in self.ioc_cache:
            cached_data, timestamp = self.ioc_cache[cache_key]
            if datetime.now() - timestamp < self.cache_expiry:
                return cached_data

        # Query threat intelligence providers
        intelligence = {
            'indicator': indicator,
            'type': self._classify_indicator(indicator),
            'reputation': 'unknown',
            'confidence': 0.0,
            'tactics': [],
            'techniques': [],
            'threat_actors': [],
            'first_seen': None,
            'last_seen': None
        }

        # Query multiple providers
        for provider_name, provider_func in self.providers.items():
            try:
                provider_intel = await provider_func(indicator)
                intelligence = self._merge_intelligence(intelligence, provider_intel)
            except Exception as e:
                logging.warning(f"Failed to query {provider_name} for {indicator}: {str(e)}")

        # Cache result
        self.ioc_cache[cache_key] = (intelligence, datetime.now())

        return intelligence

    def _classify_indicator(self, indicator: str) -> str:
        """Classify indicator type"""
        import re

        if re.match(r'^[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}$', indicator):
            return 'ip_address'
        elif re.match(r'^[a-fA-F0-9]{32}$', indicator):
            return 'md5_hash'
        elif re.match(r'^[a-fA-F0-9]{40}$', indicator):
            return 'sha1_hash'
        elif re.match(r'^[a-fA-F0-9]{64}$', indicator):
            return 'sha256_hash'
        elif '.' in indicator and not indicator.replace('.', '').isdigit():
            return 'domain'
        else:
            return 'unknown'

    async def _query_mitre_attack(self, indicator: str) -> Dict:
        """Query MITRE ATT&CK framework"""
        # Simulate MITRE ATT&CK API query
        return {
            'tactics': ['initial_access', 'persistence'],
            'techniques': ['T1566.001', 'T1053.005'],
            'confidence': 0.8
        }

    async def _query_virustotal(self, indicator: str) -> Dict:
        """Query VirusTotal API"""
        # Simulate VirusTotal API query
        return {
            'reputation': 'malicious',
            'confidence': 0.9,
            'first_seen': '2024-01-15',
            'detection_ratio': '45/67'
        }

class AutomatedForensics:
    """Automated forensic data collection for cloud environments"""

    def __init__(self):
        self.aws_session = boto3.Session()
        self.evidence_bucket = "incident-response-evidence"
        self.forensic_tools = {
            'memory_dump': self._collect_memory_dump,
            'disk_image': self._collect_disk_image,
            'network_capture': self._collect_network_capture,
            'container_logs': self._collect_container_logs,
            'api_logs': self._collect_api_logs,
            'configuration_snapshot': self._collect_configuration_snapshot
        }

    async def collect_evidence(self, incident: IncidentCase, evidence_types: List[str]) -> Dict:
        """Collect forensic evidence for incident"""

        evidence_collection_id = f"evidence_{incident.incident_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"

        collection_results = {
            'collection_id': evidence_collection_id,
            'incident_id': incident.incident_id,
            'collection_timestamp': datetime.now().isoformat(),
            'evidence_items': [],
            'chain_of_custody': [],
            'collection_status': 'in_progress'
        }

        # Collect each type of evidence
        for evidence_type in evidence_types:
            if evidence_type in self.forensic_tools:
                try:
                    evidence_item = await self.forensic_tools[evidence_type](incident)
                    collection_results['evidence_items'].append(evidence_item)

                    # Record chain of custody
                    custody_record = {
                        'evidence_id': evidence_item['evidence_id'],
                        'collected_by': 'automated_system',
                        'collection_timestamp': datetime.now().isoformat(),
                        'evidence_hash': evidence_item.get('hash'),
                        'collection_method': evidence_type,
                        'integrity_verified': True
                    }
                    collection_results['chain_of_custody'].append(custody_record)

                except Exception as e:
                    logging.error(f"Failed to collect {evidence_type}: {str(e)}")
                    collection_results['evidence_items'].append({
                        'evidence_type': evidence_type,
                        'collection_status': 'failed',
                        'error': str(e)
                    })

        collection_results['collection_status'] = 'completed'

        # Store evidence metadata
        await self._store_evidence_metadata(collection_results)

        return collection_results

    async def _collect_container_logs(self, incident: IncidentCase) -> Dict:
        """Collect container logs for affected services"""

        evidence_item = {
            'evidence_id': str(uuid.uuid4()),
            'evidence_type': 'container_logs',
            'collection_timestamp': datetime.now().isoformat(),
            'container_data': [],
            'log_sources': []
        }

        # Extract affected containers from incident events
        affected_containers = []
        for event in incident.events:
            if 'container_id' in event.raw_data:
                affected_containers.append(event.raw_data['container_id'])
            if 'pod_name' in event.raw_data:
                affected_containers.append(event.raw_data['pod_name'])

        # Collect logs from each container
        for container in set(affected_containers):
            try:
                # Simulate container log collection
                log_data = await self._extract_container_logs(container)
                evidence_item['container_data'].append({
                    'container_id': container,
                    'log_lines': len(log_data),
                    'log_file_path': f"s3://{self.evidence_bucket}/container_logs/{container}.log",
                    'collection_status': 'success'
                })
                evidence_item['log_sources'].append(container)

            except Exception as e:
                evidence_item['container_data'].append({
                    'container_id': container,
                    'collection_status': 'failed',
                    'error': str(e)
                })

        # Calculate evidence hash
        evidence_item['hash'] = self._calculate_evidence_hash(evidence_item)

        return evidence_item

    async def _collect_api_logs(self, incident: IncidentCase) -> Dict:
        """Collect relevant API logs from CloudTrail"""

        evidence_item = {
            'evidence_id': str(uuid.uuid4()),
            'evidence_type': 'api_logs',
            'collection_timestamp': datetime.now().isoformat(),
            'api_calls': [],
            'time_range': {}
        }

        # Determine time range for log collection
        earliest_event = min(event.timestamp for event in incident.events)
        latest_event = max(event.timestamp for event in incident.events)

        # Extend time range for context
        start_time = earliest_event - timedelta(hours=1)
        end_time = latest_event + timedelta(hours=1)

        evidence_item['time_range'] = {
            'start': start_time.isoformat(),
            'end': end_time.isoformat()
        }

        # Collect CloudTrail logs
        try:
            cloudtrail = self.aws_session.client('cloudtrail')

            # Query relevant API calls
            events = cloudtrail.lookup_events(
                LookupAttributes=[
                    {
                        'AttributeKey': 'ReadOnly',
                        'AttributeValue': 'false'  # Focus on write operations
                    }
                ],
                StartTime=start_time,
                EndTime=end_time,
                MaxItems=1000
            )

            for event in events.get('Events', []):
                api_call = {
                    'event_time': event['EventTime'].isoformat(),
                    'event_name': event['EventName'],
                    'user_identity': event.get('UserIdentity', {}),
                    'source_ip': event.get('SourceIPAddress'),
                    'user_agent': event.get('UserAgent'),
                    'aws_region': event.get('AwsRegion'),
                    'event_source': event.get('EventSource'),
                    'resources': event.get('Resources', [])
                }
                evidence_item['api_calls'].append(api_call)

        except Exception as e:
            logging.error(f"Failed to collect API logs: {str(e)}")
            evidence_item['collection_error'] = str(e)

        evidence_item['hash'] = self._calculate_evidence_hash(evidence_item)

        return evidence_item

    async def _extract_container_logs(self, container_id: str) -> List[str]:
        """Extract logs from specific container"""
        # Simulate container log extraction
        # In production, this would integrate with container runtime (Docker, containerd)
        # or orchestration platform (Kubernetes, ECS)

        return [
            f"2024-06-10 10:00:01 INFO Starting application",
            f"2024-06-10 10:00:02 WARN Suspicious activity detected",
            f"2024-06-10 10:00:03 ERROR Security violation: {container_id}"
        ]

    def _calculate_evidence_hash(self, evidence_data: Dict) -> str:
        """Calculate hash for evidence integrity"""
        import hashlib

        # Create reproducible string from evidence data
        evidence_string = json.dumps(evidence_data, sort_keys=True, default=str)
        return hashlib.sha256(evidence_string.encode()).hexdigest()

    async def _store_evidence_metadata(self, collection_results: Dict):
        """Store evidence collection metadata"""
        # In production, store in secure evidence management system
        logging.info(f"Evidence collection completed: {collection_results['collection_id']}")

class IncidentPlaybook:
    """Automated incident response playbook execution"""

    def __init__(self, playbook_id: str, name: str, triggers: List[str]):
        self.playbook_id = playbook_id
        self.name = name
        self.triggers = triggers
        self.steps = []
        self.conditions = {}

    def add_step(self, step_id: str, action: ResponseAction,
                 parameters: Dict, conditions: Dict = None):
        """Add step to playbook"""

        step = {
            'step_id': step_id,
            'action': action,
            'parameters': parameters,
            'conditions': conditions or {},
            'timeout': parameters.get('timeout', 300),  # 5 minutes default
            'retry_count': parameters.get('retry_count', 3),
            'critical': parameters.get('critical', False)
        }

        self.steps.append(step)

    async def execute(self, incident: IncidentCase, context: Dict = None) -> Dict:
        """Execute playbook for incident"""

        execution_id = f"exec_{self.playbook_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"

        execution_results = {
            'execution_id': execution_id,
            'playbook_id': self.playbook_id,
            'incident_id': incident.incident_id,
            'start_time': datetime.now().isoformat(),
            'context': context or {},
            'step_results': [],
            'overall_status': 'running',
            'end_time': None
        }

        logging.info(f"Executing playbook {self.name} for incident {incident.incident_id}")

        for step in self.steps:
            step_start = datetime.now()

            # Check step conditions
            if not self._evaluate_conditions(step.get('conditions', {}), incident, context):
                execution_results['step_results'].append({
                    'step_id': step['step_id'],
                    'status': 'skipped',
                    'reason': 'conditions_not_met',
                    'execution_time': 0
                })
                continue

            # Execute step with retries
            step_result = await self._execute_step(step, incident, context)
            step_result['execution_time'] = (datetime.now() - step_start).total_seconds()

            execution_results['step_results'].append(step_result)

            # Stop execution if critical step fails
            if step.get('critical', False) and step_result['status'] == 'failed':
                execution_results['overall_status'] = 'failed'
                break

        execution_results['end_time'] = datetime.now().isoformat()

        if execution_results['overall_status'] != 'failed':
            execution_results['overall_status'] = 'completed'

        return execution_results

    def _evaluate_conditions(self, conditions: Dict, incident: IncidentCase, context: Dict) -> bool:
        """Evaluate step execution conditions"""

        if not conditions:
            return True

        # Check severity condition
        if 'min_severity' in conditions:
            severity_levels = {'info': 1, 'low': 2, 'medium': 3, 'high': 4, 'critical': 5}
            incident_level = severity_levels.get(incident.severity.value, 0)
            required_level = severity_levels.get(conditions['min_severity'], 0)

            if incident_level < required_level:
                return False

        # Check indicator conditions
        if 'required_indicators' in conditions:
            incident_indicators = set()
            for event in incident.events:
                incident_indicators.update(event.indicators)

            required_indicators = set(conditions['required_indicators'])
            if not required_indicators.issubset(incident_indicators):
                return False

        # Check context conditions
        if 'context_requirements' in conditions:
            for key, value in conditions['context_requirements'].items():
                if context.get(key) != value:
                    return False

        return True

    async def _execute_step(self, step: Dict, incident: IncidentCase, context: Dict) -> Dict:
        """Execute individual playbook step"""

        step_result = {
            'step_id': step['step_id'],
            'action': step['action'].value,
            'status': 'running',
            'attempts': 0,
            'error': None,
            'output': {}
        }

        # Execute step with retries
        for attempt in range(step['retry_count']):
            step_result['attempts'] = attempt + 1

            try:
                # Execute step based on action type
                if step['action'] == ResponseAction.INVESTIGATE:
                    output = await self._execute_investigate(step['parameters'], incident)
                elif step['action'] == ResponseAction.CONTAIN:
                    output = await self._execute_contain(step['parameters'], incident)
                elif step['action'] == ResponseAction.ISOLATE:
                    output = await self._execute_isolate(step['parameters'], incident)
                elif step['action'] == ResponseAction.NOTIFY:
                    output = await self._execute_notify(step['parameters'], incident)
                elif step['action'] == ResponseAction.REMEDIATE:
                    output = await self._execute_remediate(step['parameters'], incident)
                else:
                    raise ValueError(f"Unknown action: {step['action']}")

                step_result['status'] = 'success'
                step_result['output'] = output
                break

            except Exception as e:
                step_result['error'] = str(e)
                logging.warning(f"Step {step['step_id']} attempt {attempt + 1} failed: {str(e)}")

                if attempt == step['retry_count'] - 1:
                    step_result['status'] = 'failed'
                else:
                    await asyncio.sleep(2 ** attempt)  # Exponential backoff

        return step_result

    async def _execute_investigate(self, parameters: Dict, incident: IncidentCase) -> Dict:
        """Execute investigation step"""

        investigation_results = {
            'investigation_type': parameters.get('type', 'general'),
            'findings': [],
            'enrichment_data': {},
            'confidence_score': 0.0
        }

        # Threat intelligence enrichment
        if 'enrich_indicators' in parameters and parameters['enrich_indicators']:
            threat_intel = ThreatIntelligenceProvider()

            # Collect all indicators from incident events
            all_indicators = []
            for event in incident.events:
                all_indicators.extend(event.indicators)

            if all_indicators:
                enrichment = await threat_intel.enrich_indicators(list(set(all_indicators)))
                investigation_results['enrichment_data'] = enrichment
                investigation_results['confidence_score'] = enrichment['confidence_score']

        # Timeline analysis
        if 'timeline_analysis' in parameters and parameters['timeline_analysis']:
            timeline = self._create_incident_timeline(incident)
            investigation_results['timeline'] = timeline

        return investigation_results

    async def _execute_contain(self, parameters: Dict, incident: IncidentCase) -> Dict:
        """Execute containment step"""

        containment_results = {
            'containment_type': parameters.get('type', 'network'),
            'actions_taken': [],
            'affected_resources': [],
            'status': 'success'
        }

        # Network containment
        if parameters.get('type') == 'network':
            # Block malicious IPs
            if 'block_ips' in parameters:
                for ip in parameters['block_ips']:
                    # Simulate IP blocking via WAF/Security Groups
                    containment_results['actions_taken'].append(f"Blocked IP: {ip}")

            # Isolate affected instances
            if 'isolate_instances' in parameters:
                for instance_id in parameters['isolate_instances']:
                    # Simulate instance isolation
                    containment_results['actions_taken'].append(f"Isolated instance: {instance_id}")
                    containment_results['affected_resources'].append(instance_id)

        # Container containment
        elif parameters.get('type') == 'container':
            # Stop malicious containers
            if 'stop_containers' in parameters:
                for container_id in parameters['stop_containers']:
                    containment_results['actions_taken'].append(f"Stopped container: {container_id}")
                    containment_results['affected_resources'].append(container_id)

        return containment_results

    async def _execute_notify(self, parameters: Dict, incident: IncidentCase) -> Dict:
        """Execute notification step"""

        notification_results = {
            'notifications_sent': [],
            'notification_channels': [],
            'status': 'success'
        }

        # Email notifications
        if 'email' in parameters:
            for recipient in parameters['email'].get('recipients', []):
                # Simulate email notification
                notification_results['notifications_sent'].append({
                    'channel': 'email',
                    'recipient': recipient,
                    'subject': f"Security Incident: {incident.title}",
                    'timestamp': datetime.now().isoformat()
                })

        # Slack notifications
        if 'slack' in parameters:
            channel = parameters['slack'].get('channel', '#security-alerts')
            # Simulate Slack notification
            notification_results['notifications_sent'].append({
                'channel': 'slack',
                'slack_channel': channel,
                'message': f"🚨 Security Incident: {incident.title} (Severity: {incident.severity.value})",
                'timestamp': datetime.now().isoformat()
            })

        # PagerDuty escalation
        if 'pagerduty' in parameters and incident.severity in ['critical', 'high']:
            notification_results['notifications_sent'].append({
                'channel': 'pagerduty',
                'service_key': parameters['pagerduty'].get('service_key'),
                'escalation_policy': 'security_team',
                'timestamp': datetime.now().isoformat()
            })

        return notification_results

    def _create_incident_timeline(self, incident: IncidentCase) -> List[Dict]:
        """Create chronological timeline of incident events"""

        timeline_events = []

        # Add all security events to timeline
        for event in incident.events:
            timeline_events.append({
                'timestamp': event.timestamp.isoformat(),
                'event_type': 'security_event',
                'source': event.source,
                'description': f"{event.event_type} from {event.source}",
                'severity': event.severity.value,
                'indicators': event.indicators
            })

        # Add response actions to timeline
        for action in incident.actions_taken:
            timeline_events.append({
                'timestamp': action.get('timestamp', datetime.now().isoformat()),
                'event_type': 'response_action',
                'action': action.get('action'),
                'description': action.get('description', ''),
                'status': action.get('status')
            })

        # Sort by timestamp
        timeline_events.sort(key=lambda x: x['timestamp'])

        return timeline_events

class SOARPlatform:
    """Main Security Orchestration, Automation and Response platform"""

    def __init__(self):
        self.incidents = {}
        self.playbooks = {}
        self.threat_intel = ThreatIntelligenceProvider()
        self.forensics = AutomatedForensics()

        # Initialize default playbooks
        self._initialize_default_playbooks()

        # ML models for incident classification
        self.ml_models = {
            'severity_classifier': None,  # Would load pre-trained model
            'false_positive_detector': None,
            'incident_classifier': None
        }

    def _initialize_default_playbooks(self):
        """Initialize default incident response playbooks"""

        # Malware detection playbook
        malware_playbook = IncidentPlaybook(
            playbook_id="malware_response",
            name="Malware Detection Response",
            triggers=["malware_detected", "suspicious_file", "ransomware_detected"]
        )

        malware_playbook.add_step(
            "investigate_malware",
            ResponseAction.INVESTIGATE,
            {
                "type": "malware_analysis",
                "enrich_indicators": True,
                "timeline_analysis": True,
                "timeout": 300
            }
        )

        malware_playbook.add_step(
            "contain_malware",
            ResponseAction.CONTAIN,
            {
                "type": "network",
                "block_ips": [],  # Will be populated from investigation
                "isolate_instances": [],
                "critical": True
            },
            conditions={"min_severity": "medium"}
        )

        malware_playbook.add_step(
            "notify_security_team",
            ResponseAction.NOTIFY,
            {
                "email": {
                    "recipients": ["security-team@company.com"]
                },
                "slack": {
                    "channel": "#security-incidents"
                },
                "pagerduty": {
                    "service_key": "malware_service_key"
                }
            }
        )

        self.playbooks["malware_response"] = malware_playbook

        # Data exfiltration playbook
        exfiltration_playbook = IncidentPlaybook(
            playbook_id="data_exfiltration_response",
            name="Data Exfiltration Response",
            triggers=["data_exfiltration", "unusual_data_transfer", "insider_threat"]
        )

        exfiltration_playbook.add_step(
            "investigate_data_access",
            ResponseAction.INVESTIGATE,
            {
                "type": "data_access_analysis",
                "enrich_indicators": True,
                "user_behavior_analysis": True
            }
        )

        exfiltration_playbook.add_step(
            "isolate_affected_accounts",
            ResponseAction.ISOLATE,
            {
                "type": "account_isolation",
                "disable_accounts": [],
                "revoke_tokens": True,
                "critical": True
            },
            conditions={"min_severity": "high"}
        )

        self.playbooks["data_exfiltration_response"] = exfiltration_playbook

    async def process_security_event(self, event: SecurityEvent) -> Optional[IncidentCase]:
        """Process incoming security event and determine if incident should be created"""

        # Check if event matches existing incident
        existing_incident = await self._find_related_incident(event)

        if existing_incident:
            # Add event to existing incident
            existing_incident.events.append(event)
            existing_incident.updated_at = datetime.now()

            # Re-evaluate incident severity
            await self._update_incident_severity(existing_incident)

            return existing_incident

        # Determine if event should create new incident
        should_create_incident = await self._evaluate_incident_creation(event)

        if should_create_incident:
            # Create new incident
            incident = IncidentCase(
                incident_id=str(uuid.uuid4()),
                title=self._generate_incident_title(event),
                description=self._generate_incident_description(event),
                severity=event.severity,
                status=IncidentStatus.OPEN,
                created_at=datetime.now(),
                updated_at=datetime.now(),
                events=[event]
            )

            self.incidents[incident.incident_id] = incident

            # Trigger automated response
            await self._trigger_automated_response(incident)

            return incident

        return None

    async def _find_related_incident(self, event: SecurityEvent) -> Optional[IncidentCase]:
        """Find if event is related to existing incident"""

        # Look for incidents with similar indicators
        for incident in self.incidents.values():
            if incident.status in [IncidentStatus.CLOSED, IncidentStatus.RESOLVED]:
                continue

            # Check for common indicators
            incident_indicators = set()
            for inc_event in incident.events:
                incident_indicators.update(inc_event.indicators)

            common_indicators = incident_indicators.intersection(set(event.indicators))

            if common_indicators:
                return incident

            # Check for temporal proximity and source similarity
            time_window = timedelta(hours=2)
            for inc_event in incident.events:
                if (abs(event.timestamp - inc_event.timestamp) < time_window and
                    event.source == inc_event.source):
                    return incident

        return None

    async def _evaluate_incident_creation(self, event: SecurityEvent) -> bool:
        """Evaluate if security event should trigger incident creation"""

        # Always create incident for critical events
        if event.severity == IncidentSeverity.CRITICAL:
            return True

        # Use ML model for false positive detection
        if self.ml_models.get('false_positive_detector'):
            # In production, use trained ML model
            false_positive_probability = 0.1  # Simulate ML prediction
            if false_positive_probability > 0.8:
                return False

        # Check threat intelligence confidence
        if event.indicators:
            threat_intel = await self.threat_intel.enrich_indicators(event.indicators)
            if threat_intel['confidence_score'] > 0.7:
                return True

        # High severity events with context
        if event.severity == IncidentSeverity.HIGH and len(event.indicators) > 0:
            return True

        return False

    async def _trigger_automated_response(self, incident: IncidentCase):
        """Trigger appropriate automated response playbook"""

        # Determine applicable playbooks
        applicable_playbooks = []

        for playbook in self.playbooks.values():
            # Check if any event triggers match playbook
            for event in incident.events:
                if event.event_type in playbook.triggers:
                    applicable_playbooks.append(playbook)
                    break

        # Execute applicable playbooks
        for playbook in applicable_playbooks:
            try:
                execution_result = await playbook.execute(incident)

                # Record playbook execution
                incident.actions_taken.append({
                    'action': 'playbook_execution',
                    'playbook_id': playbook.playbook_id,
                    'execution_id': execution_result['execution_id'],
                    'status': execution_result['overall_status'],
                    'timestamp': datetime.now().isoformat()
                })

                logging.info(f"Executed playbook {playbook.name} for incident {incident.incident_id}")

            except Exception as e:
                logging.error(f"Failed to execute playbook {playbook.name}: {str(e)}")

    def _generate_incident_title(self, event: SecurityEvent) -> str:
        """Generate descriptive incident title"""
        return f"{event.event_type.replace('_', ' ').title()} - {event.source}"

    def _generate_incident_description(self, event: SecurityEvent) -> str:
        """Generate incident description"""
        return f"Security event detected: {event.event_type} from {event.source} at {event.timestamp}"

    async def get_incident_status(self, incident_id: str) -> Dict:
        """Get comprehensive incident status"""

        if incident_id not in self.incidents:
            raise ValueError(f"Incident {incident_id} not found")

        incident = self.incidents[incident_id]

        status = {
            'incident_id': incident_id,
            'title': incident.title,
            'severity': incident.severity.value,
            'status': incident.status.value,
            'created_at': incident.created_at.isoformat(),
            'updated_at': incident.updated_at.isoformat(),
            'event_count': len(incident.events),
            'actions_taken': len(incident.actions_taken),
            'timeline': self._create_incident_timeline(incident),
            'threat_intelligence': {},
            'evidence_collected': len(incident.evidence)
        }

        # Add threat intelligence summary
        all_indicators = []
        for event in incident.events:
            all_indicators.extend(event.indicators)

        if all_indicators:
            threat_intel = await self.threat_intel.enrich_indicators(list(set(all_indicators)))
            status['threat_intelligence'] = {
                'confidence_score': threat_intel['confidence_score'],
                'tactics': list(set(threat_intel['tactics'])),
                'techniques': list(set(threat_intel['techniques'])),
                'threat_actors': list(set(threat_intel['threat_actors']))
            }

        return status

    def _create_incident_timeline(self, incident: IncidentCase) -> List[Dict]:
        """Create incident timeline (same as in playbook)"""
        # Implementation same as in IncidentPlaybook._create_incident_timeline
        pass

# Example usage and integration
if __name__ == "__main__":
    import asyncio

    async def demonstrate_soar_platform():
        # Initialize SOAR platform
        soar = SOARPlatform()

        # Simulate security event
        malware_event = SecurityEvent(
            event_id=str(uuid.uuid4()),
            timestamp=datetime.now(),
            source="endpoint_detection",
            event_type="malware_detected",
            severity=IncidentSeverity.HIGH,
            raw_data={
                "file_hash": "a1b2c3d4e5f6",
                "file_path": "/tmp/suspicious.exe",
                "process_id": 1234,
                "host_id": "web-server-01"
            },
            indicators=["192.168.1.100", "malware.example.com", "a1b2c3d4e5f6"],
            confidence_score=0.9
        )

        # Process event through SOAR platform
        incident = await soar.process_security_event(malware_event)

        if incident:
            print(f"Created incident: {incident.incident_id}")
            print(f"Title: {incident.title}")
            print(f"Severity: {incident.severity.value}")

            # Get incident status
            status = await soar.get_incident_status(incident.incident_id)
            print(f"Actions taken: {status['actions_taken']}")

        # Collect forensic evidence
        evidence_types = ['container_logs', 'api_logs', 'configuration_snapshot']
        evidence = await soar.forensics.collect_evidence(incident, evidence_types)

        print(f"Evidence collected: {len(evidence['evidence_items'])} items")

    # Run demonstration
    asyncio.run(demonstrate_soar_platform())

2. Machine Learning-Enhanced Threat Detection

# incident-response/ml_threat_detection.py
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest, RandomForestClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split
from typing import Dict, List, Tuple, Optional
import joblib
import logging
from datetime import datetime, timedelta
import asyncio

class MLThreatDetector:
    """Machine learning-based threat detection and classification"""

    def __init__(self):
        self.models = {
            'anomaly_detector': None,
            'severity_classifier': None,
            'false_positive_classifier': None,
            'threat_type_classifier': None
        }

        self.scalers = {}
        self.encoders = {}
        self.feature_columns = []

        # Training configuration
        self.training_config = {
            'anomaly_detection': {
                'contamination': 0.1,
                'n_estimators': 100,
                'random_state': 42
            },
            'severity_classification': {
                'n_estimators': 100,
                'max_depth': 10,
                'random_state': 42
            }
        }

    async def train_models(self, training_data: pd.DataFrame):
        """Train all ML models with security event data"""

        logging.info("Starting ML model training...")

        # Prepare features
        features = self._extract_features(training_data)

        # Train anomaly detection model
        await self._train_anomaly_detector(features)

        # Train severity classifier
        if 'severity' in training_data.columns:
            await self._train_severity_classifier(features, training_data['severity'])

        # Train false positive classifier
        if 'is_false_positive' in training_data.columns:
            await self._train_false_positive_classifier(features, training_data['is_false_positive'])

        # Train threat type classifier
        if 'threat_type' in training_data.columns:
            await self._train_threat_type_classifier(features, training_data['threat_type'])

        logging.info("ML model training completed")

    def _extract_features(self, data: pd.DataFrame) -> pd.DataFrame:
        """Extract features for ML models"""

        features = pd.DataFrame()

        # Temporal features
        if 'timestamp' in data.columns:
            data['timestamp'] = pd.to_datetime(data['timestamp'])
            features['hour_of_day'] = data['timestamp'].dt.hour
            features['day_of_week'] = data['timestamp'].dt.dayofweek
            features['is_weekend'] = (data['timestamp'].dt.dayofweek >= 5).astype(int)
            features['is_business_hours'] = ((data['timestamp'].dt.hour >= 9) &
                                           (data['timestamp'].dt.hour <= 17)).astype(int)

        # Source-based features
        if 'source' in data.columns:
            source_encoder = LabelEncoder()
            features['source_encoded'] = source_encoder.fit_transform(data['source'])
            self.encoders['source'] = source_encoder

        # Event type features
        if 'event_type' in data.columns:
            event_type_encoder = LabelEncoder()
            features['event_type_encoded'] = event_type_encoder.fit_transform(data['event_type'])
            self.encoders['event_type'] = event_type_encoder

        # Indicator count features
        if 'indicators' in data.columns:
            features['indicator_count'] = data['indicators'].apply(
                lambda x: len(x) if isinstance(x, list) else 0
            )

        # Raw data complexity features
        if 'raw_data' in data.columns:
            features['raw_data_size'] = data['raw_data'].apply(
                lambda x: len(str(x)) if x else 0
            )
            features['raw_data_fields'] = data['raw_data'].apply(
                lambda x: len(x.keys()) if isinstance(x, dict) else 0
            )

        # Network-based features
        if 'source_ip' in data.columns:
            features['is_private_ip'] = data['source_ip'].apply(self._is_private_ip)
            features['is_known_malicious_ip'] = data['source_ip'].apply(self._is_known_malicious_ip)

        # User behavior features
        if 'user_id' in data.columns:
            user_activity = data.groupby('user_id').size()
            features['user_activity_count'] = data['user_id'].map(user_activity)

            # Calculate user activity patterns
            if 'timestamp' in data.columns:
                user_hour_variance = data.groupby('user_id')['timestamp'].apply(
                    lambda x: x.dt.hour.std()
                ).fillna(0)
                features['user_hour_variance'] = data['user_id'].map(user_hour_variance)

        # Geographic features
        if 'source_country' in data.columns:
            high_risk_countries = ['CN', 'RU', 'KP', 'IR']  # Example high-risk countries
            features['is_high_risk_country'] = data['source_country'].isin(high_risk_countries).astype(int)

        # Confidence score features
        if 'confidence_score' in data.columns:
            features['confidence_score'] = data['confidence_score']
            features['high_confidence'] = (data['confidence_score'] > 0.8).astype(int)

        # Store feature columns for future use
        self.feature_columns = features.columns.tolist()

        return features

    async def _train_anomaly_detector(self, features: pd.DataFrame):
        """Train isolation forest for anomaly detection"""

        # Scale features
        scaler = StandardScaler()
        scaled_features = scaler.fit_transform(features)
        self.scalers['anomaly_detection'] = scaler

        # Train isolation forest
        isolation_forest = IsolationForest(
            **self.training_config['anomaly_detection']
        )

        isolation_forest.fit(scaled_features)
        self.models['anomaly_detector'] = isolation_forest

        logging.info("Anomaly detector training completed")

    async def _train_severity_classifier(self, features: pd.DataFrame, labels: pd.Series):
        """Train random forest for severity classification"""

        # Encode severity labels
        severity_encoder = LabelEncoder()
        encoded_labels = severity_encoder.fit_transform(labels)
        self.encoders['severity'] = severity_encoder

        # Split data
        X_train, X_test, y_train, y_test = train_test_split(
            features, encoded_labels, test_size=0.2, random_state=42
        )

        # Scale features
        scaler = StandardScaler()
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)
        self.scalers['severity_classification'] = scaler

        # Train classifier
        rf_classifier = RandomForestClassifier(
            **self.training_config['severity_classification']
        )

        rf_classifier.fit(X_train_scaled, y_train)

        # Evaluate model
        test_accuracy = rf_classifier.score(X_test_scaled, y_test)
        logging.info(f"Severity classifier accuracy: {test_accuracy:.3f}")

        self.models['severity_classifier'] = rf_classifier

    async def _train_false_positive_classifier(self, features: pd.DataFrame, labels: pd.Series):
        """Train classifier to detect false positives"""

        # Split data
        X_train, X_test, y_train, y_test = train_test_split(
            features, labels, test_size=0.2, random_state=42
        )

        # Scale features
        scaler = StandardScaler()
        X_train_scaled = scaler.fit_transform(X_train)
        X_test_scaled = scaler.transform(X_test)
        self.scalers['false_positive_classification'] = scaler

        # Train classifier
        fp_classifier = RandomForestClassifier(
            n_estimators=100,
            max_depth=8,
            random_state=42,
            class_weight='balanced'  # Handle imbalanced data
        )

        fp_classifier.fit(X_train_scaled, y_train)

        # Evaluate model
        test_accuracy = fp_classifier.score(X_test_scaled, y_test)
        logging.info(f"False positive classifier accuracy: {test_accuracy:.3f}")

        self.models['false_positive_classifier'] = fp_classifier

    async def analyze_security_event(self, event_data: Dict) -> Dict:
        """Analyze security event using trained ML models"""

        # Convert event to DataFrame for feature extraction
        event_df = pd.DataFrame([event_data])
        features = self._extract_features(event_df)

        analysis_results = {
            'event_id': event_data.get('event_id', 'unknown'),
            'timestamp': datetime.now().isoformat(),
            'ml_analysis': {}
        }

        # Anomaly detection
        if self.models['anomaly_detector']:
            anomaly_score = await self._detect_anomaly(features)
            analysis_results['ml_analysis']['anomaly_score'] = anomaly_score
            analysis_results['ml_analysis']['is_anomaly'] = anomaly_score < -0.5

        # Severity prediction
        if self.models['severity_classifier']:
            predicted_severity = await self._predict_severity(features)
            analysis_results['ml_analysis']['predicted_severity'] = predicted_severity

        # False positive detection
        if self.models['false_positive_classifier']:
            fp_probability = await self._predict_false_positive(features)
            analysis_results['ml_analysis']['false_positive_probability'] = fp_probability
            analysis_results['ml_analysis']['likely_false_positive'] = fp_probability > 0.7

        # Threat type classification
        if self.models['threat_type_classifier']:
            threat_type = await self._classify_threat_type(features)
            analysis_results['ml_analysis']['predicted_threat_type'] = threat_type

        return analysis_results

    async def _detect_anomaly(self, features: pd.DataFrame) -> float:
        """Detect anomalies using isolation forest"""

        # Scale features
        scaler = self.scalers['anomaly_detection']
        scaled_features = scaler.transform(features)

        # Get anomaly score
        anomaly_score = self.models['anomaly_detector'].decision_function(scaled_features)[0]

        return float(anomaly_score)

    async def _predict_severity(self, features: pd.DataFrame) -> str:
        """Predict incident severity"""

        # Scale features
        scaler = self.scalers['severity_classification']
        scaled_features = scaler.transform(features)

        # Predict severity
        predicted_encoded = self.models['severity_classifier'].predict(scaled_features)[0]
        predicted_severity = self.encoders['severity'].inverse_transform([predicted_encoded])[0]

        return predicted_severity

    async def _predict_false_positive(self, features: pd.DataFrame) -> float:
        """Predict probability of false positive"""

        # Scale features
        scaler = self.scalers['false_positive_classification']
        scaled_features = scaler.transform(features)

        # Predict probability
        fp_probability = self.models['false_positive_classifier'].predict_proba(scaled_features)[0][1]

        return float(fp_probability)

    def _is_private_ip(self, ip: str) -> bool:
        """Check if IP address is private"""
        try:
            import ipaddress
            ip_obj = ipaddress.ip_address(ip)
            return ip_obj.is_private
        except:
            return False

    def _is_known_malicious_ip(self, ip: str) -> bool:
        """Check if IP is in known malicious IP database"""
        # In production, query threat intelligence databases
        malicious_ips = ['192.168.100.100', '10.0.0.200']  # Example malicious IPs
        return ip in malicious_ips

    def save_models(self, model_path: str):
        """Save trained models to disk"""

        model_data = {
            'models': self.models,
            'scalers': self.scalers,
            'encoders': self.encoders,
            'feature_columns': self.feature_columns,
            'training_config': self.training_config
        }

        joblib.dump(model_data, model_path)
        logging.info(f"Models saved to {model_path}")

    def load_models(self, model_path: str):
        """Load trained models from disk"""

        model_data = joblib.load(model_path)

        self.models = model_data['models']
        self.scalers = model_data['scalers']
        self.encoders = model_data['encoders']
        self.feature_columns = model_data['feature_columns']
        self.training_config = model_data['training_config']

        logging.info(f"Models loaded from {model_path}")

# Integration with SOAR platform
class EnhancedSOARPlatform(SOARPlatform):
    """SOAR platform enhanced with ML threat detection"""

    def __init__(self):
        super().__init__()
        self.ml_detector = MLThreatDetector()

        # Try to load pre-trained models
        try:
            self.ml_detector.load_models('models/threat_detection_models.pkl')
            logging.info("Pre-trained ML models loaded successfully")
        except FileNotFoundError:
            logging.warning("No pre-trained models found. Train models before using ML features.")

    async def process_security_event(self, event: SecurityEvent) -> Optional[IncidentCase]:
        """Enhanced event processing with ML analysis"""

        # Run ML analysis on event
        ml_analysis = await self.ml_detector.analyze_security_event({
            'event_id': event.event_id,
            'timestamp': event.timestamp,
            'source': event.source,
            'event_type': event.event_type,
            'indicators': event.indicators,
            'raw_data': event.raw_data,
            'confidence_score': event.confidence_score
        })

        # Enhance event with ML insights
        event.context['ml_analysis'] = ml_analysis['ml_analysis']

        # Skip likely false positives
        if ml_analysis['ml_analysis'].get('likely_false_positive', False):
            logging.info(f"Event {event.event_id} classified as likely false positive, skipping incident creation")
            return None

        # Adjust severity based on ML prediction
        if 'predicted_severity' in ml_analysis['ml_analysis']:
            predicted_severity = ml_analysis['ml_analysis']['predicted_severity']
            if predicted_severity != event.severity.value:
                logging.info(f"ML model suggests severity adjustment: {event.severity.value} -> {predicted_severity}")
                event.severity = IncidentSeverity(predicted_severity)

        # Continue with standard processing
        return await super().process_security_event(event)

Performance Metrics and Continuous Improvement

Incident Response Metrics Dashboard

1. Real-Time Performance Monitoring

# incident-response/metrics_dashboard.py
import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
from datetime import datetime, timedelta
import numpy as np

class IncidentResponseMetrics:
    """Track and visualize incident response performance metrics"""

    def __init__(self, soar_platform):
        self.soar = soar_platform

    def create_dashboard(self):
        """Create comprehensive incident response dashboard"""

        st.set_page_config(
            page_title="Incident Response Dashboard",
            page_icon="🚨",
            layout="wide"
        )

        st.title("🚨 Security Incident Response Dashboard")
        st.markdown("Real-time monitoring of security operations and incident response effectiveness")

        # Key Performance Indicators
        self._create_kpi_section()

        # Incident trends
        self._create_trend_analysis()

        # Response performance
        self._create_response_metrics()

        # ML model performance
        self._create_ml_performance()

        # Threat intelligence insights
        self._create_threat_intelligence_section()

    def _create_kpi_section(self):
        """Create key performance indicators section"""

        st.header("📊 Key Performance Indicators")

        # Calculate metrics
        total_incidents = len(self.soar.incidents)
        critical_incidents = len([i for i in self.soar.incidents.values() if i.severity == 'critical'])
        avg_response_time = self._calculate_avg_response_time()
        false_positive_rate = self._calculate_false_positive_rate()

        col1, col2, col3, col4 = st.columns(4)

        with col1:
            st.metric(
                label="Total Incidents (30d)",
                value=total_incidents,
                delta=f"+{total_incidents - 45}" if total_incidents > 45 else f"{total_incidents - 45}"
            )

        with col2:
            st.metric(
                label="Critical Incidents",
                value=critical_incidents,
                delta=f"+{critical_incidents - 5}" if critical_incidents > 5 else f"{critical_incidents - 5}"
            )

        with col3:
            st.metric(
                label="Avg Response Time",
                value=f"{avg_response_time:.1f} min",
                delta=f"{avg_response_time - 15:.1f} min" if avg_response_time != 15 else "0 min"
            )

        with col4:
            st.metric(
                label="False Positive Rate",
                value=f"{false_positive_rate:.1f}%",
                delta=f"{false_positive_rate - 12:.1f}%" if false_positive_rate != 12 else "0%"
            )

    def _create_trend_analysis(self):
        """Create incident trend analysis"""

        st.header("📈 Incident Trends")

        # Generate sample trend data
        dates = pd.date_range(start=datetime.now() - timedelta(days=30), end=datetime.now(), freq='D')
        incident_counts = np.random.poisson(3, len(dates))
        critical_counts = np.random.poisson(0.5, len(dates))

        trend_data = pd.DataFrame({
            'date': dates,
            'total_incidents': incident_counts,
            'critical_incidents': critical_counts
        })

        col1, col2 = st.columns(2)

        with col1:
            fig_trend = px.line(
                trend_data,
                x='date',
                y=['total_incidents', 'critical_incidents'],
                title="Daily Incident Count Trends",
                labels={'value': 'Number of Incidents', 'date': 'Date'}
            )
            st.plotly_chart(fig_trend, use_container_width=True)

        with col2:
            # Incident severity distribution
            severity_data = pd.DataFrame({
                'severity': ['Critical', 'High', 'Medium', 'Low'],
                'count': [8, 25, 45, 32]
            })

            fig_severity = px.pie(
                severity_data,
                values='count',
                names='severity',
                title="Incident Severity Distribution (30d)",
                color_discrete_map={
                    'Critical': '#ff4444',
                    'High': '#ff8800',
                    'Medium': '#ffcc00',
                    'Low': '#44ff44'
                }
            )
            st.plotly_chart(fig_severity, use_container_width=True)

    def _create_response_metrics(self):
        """Create response performance metrics"""

        st.header("⚡ Response Performance")

        col1, col2 = st.columns(2)

        with col1:
            # Mean Time to Detection (MTTD)
            detection_times = np.random.exponential(10, 100)  # Sample data

            fig_mttd = go.Figure()
            fig_mttd.add_trace(go.Histogram(
                x=detection_times,
                nbinsx=20,
                name="Detection Time Distribution",
                marker_color="blue"
            ))
            fig_mttd.update_layout(
                title="Mean Time to Detection (MTTD)",
                xaxis_title="Detection Time (minutes)",
                yaxis_title="Frequency"
            )
            st.plotly_chart(fig_mttd, use_container_width=True)

        with col2:
            # Mean Time to Remediation (MTTR)
            remediation_times = np.random.exponential(45, 100)  # Sample data

            fig_mttr = go.Figure()
            fig_mttr.add_trace(go.Histogram(
                x=remediation_times,
                nbinsx=20,
                name="Remediation Time Distribution",
                marker_color="red"
            ))
            fig_mttr.update_layout(
                title="Mean Time to Remediation (MTTR)",
                xaxis_title="Remediation Time (minutes)",
                yaxis_title="Frequency"
            )
            st.plotly_chart(fig_mttr, use_container_width=True)

        # Response effectiveness over time
        st.subheader("Response Effectiveness Trends")

        effectiveness_data = pd.DataFrame({
            'week': [f"Week {i}" for i in range(1, 13)],
            'mttd': np.random.normal(12, 2, 12),
            'mttr': np.random.normal(35, 5, 12),
            'false_positive_rate': np.random.normal(15, 3, 12)
        })

        fig_effectiveness = go.Figure()

        fig_effectiveness.add_trace(go.Scatter(
            x=effectiveness_data['week'],
            y=effectiveness_data['mttd'],
            mode='lines+markers',
            name='MTTD (minutes)',
            yaxis='y'
        ))

        fig_effectiveness.add_trace(go.Scatter(
            x=effectiveness_data['week'],
            y=effectiveness_data['mttr'],
            mode='lines+markers',
            name='MTTR (minutes)',
            yaxis='y'
        ))

        fig_effectiveness.add_trace(go.Scatter(
            x=effectiveness_data['week'],
            y=effectiveness_data['false_positive_rate'],
            mode='lines+markers',
            name='False Positive Rate (%)',
            yaxis='y2'
        ))

        fig_effectiveness.update_layout(
            title="Response Effectiveness Over Time",
            xaxis_title="Time Period",
            yaxis=dict(title="Time (minutes)", side="left"),
            yaxis2=dict(title="False Positive Rate (%)", side="right", overlaying="y")
        )

        st.plotly_chart(fig_effectiveness, use_container_width=True)

    def _calculate_avg_response_time(self) -> float:
        """Calculate average response time"""
        # Simulate calculation
        return 12.5

    def _calculate_false_positive_rate(self) -> float:
        """Calculate false positive rate"""
        # Simulate calculation
        return 8.3

if __name__ == "__main__":
    # Create sample SOAR platform for dashboard
    soar = SOARPlatform()

    # Create and run metrics dashboard
    metrics = IncidentResponseMetrics(soar)
    metrics.create_dashboard()

Business Impact and ROI

Incident Response Automation ROI Analysis

Manual vs. Automated Incident Response:

Process	Manual Approach	Automated Approach	Time Savings	Cost Savings
Threat Detection	2-8 hours	5-15 minutes	92% reduction	$750K annually
Initial Response	30-60 minutes	2-5 minutes	90% reduction	$450K annually
Evidence Collection	4-8 hours	30 minutes	89% reduction	$625K annually
Threat Intelligence	2-4 hours	5 minutes	96% reduction	$300K annually
Containment Actions	1-3 hours	5-10 minutes	94% reduction	$400K annually
Forensic Analysis	8-16 hours	2 hours	85% reduction	$850K annually
Incident Documentation	2-4 hours	15 minutes	94% reduction	$250K annually

Risk Mitigation Value:

# Annual incident response automation value
THREAT_DETECTION_IMPROVEMENT = 750000    # Faster threat detection
INITIAL_RESPONSE_ACCELERATION = 450000   # Automated initial response
EVIDENCE_COLLECTION_AUTOMATION = 625000  # Automated forensics
THREAT_INTEL_AUTOMATION = 300000        # Automated threat enrichment
CONTAINMENT_AUTOMATION = 400000         # Automated containment
FORENSIC_ANALYSIS_ACCELERATION = 850000 # Automated forensic analysis
DOCUMENTATION_AUTOMATION = 250000       # Automated incident documentation
BREACH_COST_REDUCTION = 3000000         # Reduced breach impact costs

TOTAL_ANNUAL_VALUE = (THREAT_DETECTION_IMPROVEMENT + INITIAL_RESPONSE_ACCELERATION +
                     EVIDENCE_COLLECTION_AUTOMATION + THREAT_INTEL_AUTOMATION +
                     CONTAINMENT_AUTOMATION + FORENSIC_ANALYSIS_ACCELERATION +
                     DOCUMENTATION_AUTOMATION + BREACH_COST_REDUCTION)
# Total Value: $6,625,000 annually

IMPLEMENTATION_COST = 800000  # SOAR platform implementation
ANNUAL_MAINTENANCE = 160000   # Ongoing maintenance and tuning

FIRST_YEAR_ROI = ((TOTAL_ANNUAL_VALUE - IMPLEMENTATION_COST - ANNUAL_MAINTENANCE) /
                  (IMPLEMENTATION_COST + ANNUAL_MAINTENANCE)) * 100
# ROI: 590% in first year

ONGOING_ROI = ((TOTAL_ANNUAL_VALUE - ANNUAL_MAINTENANCE) / ANNUAL_MAINTENANCE) * 100
# Ongoing ROI: 4,041% annually

Conclusion

Advanced incident response automation transforms security operations from reactive firefighting into proactive threat management. By implementing intelligent detection, automated analysis, and orchestrated response capabilities, organizations can respond to threats at machine speed while maintaining human oversight for critical decisions.

The key to successful incident response automation lies in building comprehensive workflows that can adapt to evolving threats, integrating machine learning for intelligent decision-making, and maintaining continuous improvement through performance metrics and feedback loops.

Remember that incident response automation is not about replacing security analysts - it’s about amplifying their capabilities, reducing response times, and ensuring consistent, repeatable processes that improve with each incident.

Your incident response automation journey starts with implementing basic alert correlation and automated evidence collection. Begin today and build towards comprehensive security orchestration that protects your organization at machine speed.