SOC Automation for GlobalSOC

Automated SOC Architecture

Integrated Technology Stack

The platform combines multiple technologies to create a cohesive cybersecurity ecosystem:

Central SIEM: Splunk Enterprise Security as correlation and analysis core SOAR Platform: Phantom (now Splunk SOAR) for response orchestration Threat Intelligence: Automated feeds from MISP, VirusTotal, AlienVault OTX Machine Learning: Custom models for anomaly detection and classification Case Management: Integrated ticketing system with ServiceNow

Automation Flow

class SOCAutomation:
    def __init__(self):
        self.splunk = SplunkConnector()
        self.phantom = PhantomSOAR()
        self.ml_classifier = ThreatClassifier()
        
    def process_alert(self, alert):
        """Processes alerts with automatic classification"""
        
        # Contextual enrichment
        enriched_alert = self.enrich_with_context(alert)
        
        # ML classification
        threat_score = self.ml_classifier.predict(enriched_alert)
        
        # Automated decision
        if threat_score > 0.8:
            return self.escalate_to_analyst(enriched_alert)
        elif threat_score > 0.4:
            return self.automated_investigation(enriched_alert)
        else:
            return self.mark_false_positive(enriched_alert)

Automated Response Playbooks

Playbook 1: Phishing Detection & Response

Automatic Triggers:

• Detection of malicious URLs in emails
• Analysis of suspicious attachments
• Social engineering patterns

Automated Response:

1. Email quarantine in Exchange Online
2. Automatic blacklisting of malicious URLs/domains
3. Proactive notification to potentially affected users
4. Forensic analysis of related browsing logs

Performance Metrics:

• Average response time: 3.2 minutes
• Detection accuracy: 94.7%
• False positives: <2%

Playbook 2: Malware Containment

Integrated Detection:

• Endpoint detection signatures (CrowdStrike)
• Real-time behavioral analysis
• Network traffic anomalies

Containment Actions:

def malware_response_playbook(endpoint_id, malware_hash):
    """Automated playbook for malware containment"""
    
    # Immediate isolation
    isolate_endpoint(endpoint_id)
    
    # Propagation analysis
    affected_systems = analyze_lateral_movement(endpoint_id)
    
    # Threat intelligence lookup
    iocs = get_threat_intelligence(malware_hash)
    
    # Proactive hunting
    hunt_similar_threats(iocs)
    
    # Automatic documentation
    generate_incident_report(endpoint_id, malware_hash, affected_systems)

Playbook 3: Credential Compromise

Monitored Indicators:

• Multiple failed logins from unusual geolocations
• Privileged account access anomalies
• Suspicious PowerShell execution
• Data exfiltration patterns

Coordinated Response:

1. Automatic password reset for compromised accounts
2. Active token/session revocation
3. Escalation for privileged account review
4. Forensic analysis of recent activity

Machine Learning for Threat Detection

Threat Classification Model

Feature Engineering:

• Network metadata (protocols, packet sizes, timing)
• User behavior baselines (login patterns, data access)
• System performance anomalies
• Threat intelligence context scores

Implemented Algorithms:

from sklearn.ensemble import IsolationForest, RandomForestClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np

class AdvancedThreatDetector:
    def __init__(self):
        self.anomaly_detector = IsolationForest(contamination=0.1)
        self.threat_classifier = RandomForestClassifier(n_estimators=200)
        self.scaler = StandardScaler()
        
    def train_models(self, historical_data):
        """Trains models with labeled historical data"""
        features = self.extract_features(historical_data)
        features_scaled = self.scaler.fit_transform(features)
        
        # Train anomaly detector
        self.anomaly_detector.fit(features_scaled)
        
        # Train threat classifier
        labels = historical_data['threat_label']
        self.threat_classifier.fit(features_scaled, labels)
        
    def predict_threat(self, new_event):
        """Predicts if an event is legitimate threat"""
        features = self.extract_features([new_event])
        features_scaled = self.scaler.transform(features)
        
        # Detect anomaly
        anomaly_score = self.anomaly_detector.decision_function(features_scaled)[0]
        
        # Classify threat
        threat_probability = self.threat_classifier.predict_proba(features_scaled)[0]
        
        return {
            'anomaly_score': anomaly_score,
            'threat_probability': threat_probability,
            'recommended_action': self.determine_action(anomaly_score, threat_probability)
        }

ML Implementation Results

Detection Improvement:

• True Positive Rate: 96.3% (vs 78% manual)
• False Positive Rate: 3.1% (vs 67% manual)
• Mean Time to Detection: 4.7 minutes (vs 2.3 hours manual)

Threat Types Detected:

• Advanced Persistent Threats (APT): 47 unique cases
• Insider threats: 23 confirmed cases
• Zero-day exploits: 8 cases (vs 0 manual detection)
• Living-off-the-land attacks: 156 cases

Augmented Threat Hunting

Hunting Hypothesis Framework

Systematic Methodology:

1. Hypothesis Generation: Based on threat intelligence and MITRE ATT&CK
2. Data Collection: Automated hunting queries in Splunk
3. Analysis: Automated correlation with context enrichment
4. Validation: Analyst review of high-confidence findings

Hunt #1: Living-off-the-Land Detection

index=windows EventCode=4688 
| eval ProcessName=lower(split(Process_Name,"\\")[-1])
| where ProcessName IN ("powershell.exe", "cmd.exe", "wmic.exe", "certutil.exe")
| eval ProcessArgs=lower(Process_Command_Line)
| where match(ProcessArgs,"(download|invoke|iex|bypass|hidden)")
| stats count by Computer_Name, ProcessName, Process_Command_Line, user
| where count > 5
| join Computer_Name [search index=network dest_ip=Computer_Name earliest=-1h@h latest=now() | stats sum(bytes_out) as total_egress by dest_ip | rename dest_ip as Computer_Name]
| where total_egress > 50000000

Hunt #2: Lateral Movement Detection

• Automated detection of admin share access patterns
• Correlation with authentication logs
• Identification of privilege escalation chains
• Mapping to MITRE ATT&CK tactics

Threat Intelligence Integration

Automated Feed Processing:

• Daily ingestion of 50+ threat intel feeds
• Automatic IOC extraction and validation
• Context enrichment for existing alerts
• Proactive hunting based on new IOCs

Custom Intelligence Generation:

• Internal IOC generation from resolved incidents
• Behavioral signatures for industry-specific attacks
• Threat actor TTPs documentation
• Industry-specific threat landscape reports

SOC Performance Metrics

Operational KPIs

Alert Efficiency:

• Volume Reduction: 89% (from 15,000 to 1,650 alerts/day)
• Quality Improvement: 94% precision rate
• Analyst Productivity: 340% increase in throughput

Response Times:

• Mean Time to Detection (MTTD): 4.7 minutes
• Mean Time to Investigation (MTTI): 12 minutes
• Mean Time to Containment (MTTC): 23 minutes
• Mean Time to Recovery (MTTR): 1.2 hours

Threat Coverage:

threat_coverage_metrics = {
    'mitre_techniques_covered': 78,  # out of 189 total
    'detection_rules_active': 1247,
    'threat_actors_monitored': 45,
    'industry_specific_threats': 34
}

Automation ROI

Avoided Costs:

• Required staff reduction: €890k/year
• Faster incident response: €2.3M in avoided downtime
• Reduced false positives: €450k in analyst time

Quantifiable Benefits:

• 56% reduction in employee turnover
• 78% improvement in job satisfaction scores
• 340% increase in detected threats
• 67% reduction in time-to-hire for new analysts

Executive Dashboard

Real-time Metrics

Security Posture Overview:

• Current threat level indicator
• Active incidents by severity
• MITRE ATT&CK heat map
• Compliance status indicators

Operational Metrics:

• SOC analyst workload distribution
• Automation success rates
• MTTR trends by incident type
• Technology stack health status

Executive Alerts

Automatic Executive Notifications:

• Critical incidents with potential business impact
• Advanced threats requiring board awareness
• Compliance violations with regulatory implications
• Budget deviations in security spend

Lessons Learned

Critical Success Factors

Change Management: 67% of success depended on analyst buy-in Gradual Implementation: Phased approach avoided operational disruption Continuous Training: Analyst upskilling in new technologies Feedback Loops: Regular optimization based on analyst feedback

Overcome Challenges

Analyst Resistance: Initial skepticism about job displacement

• Solution: Rebranding as “analyst augmentation”, career path evolution

False Positive Tuning: 6 months for optimal threshold settings

• Solution: A/B testing of classification thresholds, continuous learning

Integration Complexity: 23 different security tools integration

• Solution: API-first approach, standardized data formats

Skills Gap: Lack of SOAR/automation expertise internally

• Solution: Intensive training program, external consulting during transition

The SOC automation project established a new industry benchmark, demonstrating that it’s possible to combine operational efficiency with improved detection capabilities while maintaining and elevating analyst team morale.