Zero Trust Security with Wazuh: Micro-Segmentation Detection & Network Correlation
Introduction
Zero Trust security has evolved from a buzzword to a critical necessity in 2025’s threat landscape. With 76% of organizations experiencing lateral movement attacks and traditional perimeter security proving ineffective, implementing Zero Trust principles with continuous verification is paramount. This comprehensive guide demonstrates how Wazuh’s advanced correlation capabilities enable micro-segmentation monitoring, identity-based access control verification, and east-west traffic analysis to achieve true Zero Trust security.
Zero Trust Architecture with Wazuh
Core Zero Trust Principles Integration
# Zero Trust Security Framework
class ZeroTrustFramework:
def __init__(self):
self.principles = {
'verify_explicitly': self.continuous_verification,
'least_privilege': self.privilege_monitoring,
'assume_breach': self.breach_detection,
'segment_everything': self.micro_segmentation,
'encrypt_everywhere': self.encryption_validation,
'continuous_monitoring': self.real_time_analysis
}
self.trust_score_calculator = TrustScoreEngine()
self.segmentation_monitor = SegmentationMonitor()
def assess_zero_trust_posture(self, event):
"""Assess event against Zero Trust principles"""
assessment = {
'timestamp': datetime.now(),
'trust_score': 0,
'violations': [],
'recommendations': []
}
# Evaluate each principle
for principle, evaluator in self.principles.items():
result = evaluator(event)
if not result['compliant']:
assessment['violations'].append({
'principle': principle,
'severity': result['severity'],
'details': result['details']
})
# Update trust score
assessment['trust_score'] += result['score_contribution']
# Generate recommendations
assessment['recommendations'] = self.generate_recommendations(
assessment['violations']
)
return assessment
Micro-Segmentation Monitoring
Network Segment Violation Detection
<!-- Zero Trust Network Segmentation Rules -->
<group name="zero_trust,micro_segmentation">
<!-- Unauthorized Cross-Segment Communication -->
<rule id="800001" level="12">
<if_sid>86001</if_sid>
<field name="network.source_zone">dmz</field>
<field name="network.dest_zone">internal</field>
<field name="action">allow</field>
<description>Zero Trust Violation: DMZ to Internal communication allowed</description>
<group>zero_trust,segmentation_breach</group>
<mitre>
<id>T1021</id>
</mitre>
</rule>
<!-- Database Direct Access Violation -->
<rule id="800002" level="14">
<if_sid>86001</if_sid>
<field name="network.dest_zone">database</field>
<field name="network.source_zone" negate="yes">application</field>
<field name="dest.port">^(3306|5432|1521|1433)$</field>
<description>Zero Trust Violation: Direct database access from unauthorized zone</description>
<group>zero_trust,critical_violation</group>
</rule>
<!-- Lateral Movement Detection -->
<rule id="800003" level="11">
<if_sid>86001</if_sid>
<field name="network.source_zone">workstation</field>
<field name="network.dest_zone">workstation</field>
<field name="dest.port">^(445|135|139|3389)$</field>
<description>Zero Trust Alert: Workstation-to-workstation SMB/RDP traffic</description>
<group>zero_trust,lateral_movement</group>
<mitre>
<id>T1021.002</id>
</mitre>
</rule>
<!-- Multi-Segment Traversal -->
<rule id="800004" level="13" frequency="3" timeframe="300">
<if_matched_rules>800001,800002,800003</if_matched_rules>
<same_source_ip />
<description>Zero Trust Critical: Multiple segment violations from same source</description>
<group>zero_trust,advanced_threat</group>
</rule>
</group>
Dynamic Segment Monitoring
class MicroSegmentationMonitor:
def __init__(self):
self.segment_map = self.load_segment_topology()
self.allowed_flows = self.load_allowed_flows()
self.ml_analyzer = SegmentationMLAnalyzer()
def analyze_traffic_flow(self, flow):
"""Analyze traffic flow for segmentation violations"""
analysis = {
'flow_id': flow['id'],
'timestamp': flow['timestamp'],
'compliance': True,
'risk_score': 0,
'violations': []
}
# Determine segments
source_segment = self.identify_segment(flow['source_ip'])
dest_segment = self.identify_segment(flow['dest_ip'])
# Check if flow is allowed
flow_key = f"{source_segment}->{dest_segment}:{flow['dest_port']}"
if flow_key not in self.allowed_flows:
analysis['compliance'] = False
analysis['violations'].append({
'type': 'unauthorized_flow',
'severity': self.calculate_severity(source_segment, dest_segment),
'description': f"Unauthorized flow from {source_segment} to {dest_segment}"
})
# ML-based anomaly detection
ml_result = self.ml_analyzer.analyze_flow_pattern(flow)
if ml_result['anomaly_score'] > 0.8:
analysis['violations'].append({
'type': 'behavioral_anomaly',
'severity': 'high',
'description': ml_result['description']
})
# Calculate risk score
analysis['risk_score'] = self.calculate_risk_score(analysis['violations'])
return analysis
def detect_segmentation_bypass(self, flows):
"""Detect attempts to bypass segmentation"""
bypass_patterns = []
# Group flows by source
source_groups = defaultdict(list)
for flow in flows:
source_groups[flow['source_ip']].append(flow)
# Analyze patterns
for source, source_flows in source_groups.items():
# Check for proxy bypass attempts
if self.detect_proxy_bypass(source_flows):
bypass_patterns.append({
'type': 'proxy_bypass',
'source': source,
'confidence': 0.9
})
# Check for tunnel detection
if self.detect_tunneling(source_flows):
bypass_patterns.append({
'type': 'protocol_tunneling',
'source': source,
'confidence': 0.85
})
return bypass_patterns
Identity-Based Access Verification
Continuous Identity Validation
<!-- Zero Trust Identity Verification Rules -->
<group name="zero_trust,identity">
<!-- Service Account Interactive Login -->
<rule id="800010" level="12">
<if_sid>18104</if_sid>
<field name="win.eventdata.targetUserName" type="pcre2">^(svc_|service_|app_)</field>
<field name="win.eventdata.logonType">^(2|10)$</field>
<description>Zero Trust Violation: Service account interactive login</description>
<group>zero_trust,identity_abuse</group>
<mitre>
<id>T1078.002</id>
</mitre>
</rule>
<!-- Privileged Account from Untrusted Location -->
<rule id="800011" level="13">
<if_sid>18104</if_sid>
<field name="win.eventdata.targetUserName" type="pcre2">^(admin|root|administrator)</field>
<field name="srcip" negate="yes">10.0.1.0/24</field>
<description>Zero Trust Violation: Admin login from untrusted network</description>
<group>zero_trust,privileged_access</group>
</rule>
<!-- MFA Bypass Detection -->
<rule id="800012" level="14">
<if_sid>18104</if_sid>
<field name="authentication.mfa_used">false</field>
<field name="user.privileges">high</field>
<description>Zero Trust Critical: High privilege login without MFA</description>
<group>zero_trust,mfa_bypass</group>
</rule>
<!-- Impossible Travel -->
<rule id="800013" level="11">
<if_sid>18104</if_sid>
<field name="geo.distance_from_last" compare=">=">500</field>
<field name="time.since_last_login" compare="<=">3600</field>
<description>Zero Trust Alert: Impossible travel detected</description>
<group>zero_trust,impossible_travel</group>
</rule>
</group>
Trust Score Calculation
class TrustScoreEngine:
def __init__(self):
self.factors = {
'device_trust': 0.2,
'user_behavior': 0.3,
'location': 0.15,
'authentication_strength': 0.25,
'time_context': 0.1
}
self.baseline_calculator = BaselineCalculator()
def calculate_trust_score(self, session):
"""Calculate dynamic trust score for session"""
trust_score = 0
factors_detail = {}
# Device trust evaluation
device_score = self.evaluate_device_trust(session['device'])
trust_score += device_score * self.factors['device_trust']
factors_detail['device'] = device_score
# User behavior analysis
behavior_score = self.analyze_user_behavior(session['user'])
trust_score += behavior_score * self.factors['user_behavior']
factors_detail['behavior'] = behavior_score
# Location assessment
location_score = self.assess_location(session['location'])
trust_score += location_score * self.factors['location']
factors_detail['location'] = location_score
# Authentication strength
auth_score = self.evaluate_authentication(session['auth_method'])
trust_score += auth_score * self.factors['authentication_strength']
factors_detail['authentication'] = auth_score
# Time context
time_score = self.evaluate_time_context(session['timestamp'])
trust_score += time_score * self.factors['time_context']
factors_detail['time'] = time_score
return {
'overall_score': trust_score,
'factors': factors_detail,
'risk_level': self.determine_risk_level(trust_score),
'recommendations': self.generate_recommendations(trust_score, factors_detail)
}
def evaluate_device_trust(self, device):
"""Evaluate device trustworthiness"""
score = 1.0
# Check device compliance
if not device.get('compliant', False):
score -= 0.4
# Check patch level
if device.get('days_since_patch', 0) > 30:
score -= 0.2
# Check encryption
if not device.get('encrypted', False):
score -= 0.3
# Check managed status
if not device.get('managed', False):
score -= 0.1
return max(0, score)
East-West Traffic Analysis
Lateral Movement Detection
<!-- East-West Traffic Monitoring -->
<group name="zero_trust,east_west">
<!-- Abnormal Internal Scanning -->
<rule id="800020" level="10" frequency="50" timeframe="300">
<if_sid>86001</if_sid>
<field name="network.direction">internal</field>
<same_source_ip />
<different_dst_port />
<description>Zero Trust: Internal port scanning detected</description>
<group>zero_trust,reconnaissance</group>
<mitre>
<id>T1046</id>
</mitre>
</rule>
<!-- Service Account Lateral Movement -->
<rule id="800021" level="12">
<if_sid>18107</if_sid>
<field name="win.eventdata.targetUserName" type="pcre2">^(svc_|service_)</field>
<field name="win.eventdata.ipAddress" negate="yes">designated_service_host</field>
<description>Zero Trust: Service account login from unexpected host</description>
<group>zero_trust,lateral_movement</group>
</rule>
<!-- Admin Tool Usage from Non-Admin Host -->
<rule id="800022" level="13">
<if_sid>86001</if_sid>
<field name="process.name" type="pcre2">(psexec|wmic|winrm|powershell)</field>
<field name="source.zone">workstation</field>
<field name="dest.zone" negate="yes">workstation</field>
<description>Zero Trust: Admin tools used from workstation to server</description>
<group>zero_trust,privileged_tools</group>
<mitre>
<id>T1021</id>
</mitre>
</rule>
</group>
Traffic Pattern Analysis
class EastWestTrafficAnalyzer:
def __init__(self):
self.graph_analyzer = NetworkGraphAnalyzer()
self.pattern_detector = PatternDetector()
self.ml_model = EastWestMLModel()
def analyze_internal_traffic(self, traffic_flows):
"""Analyze east-west traffic patterns"""
analysis = {
'timestamp': datetime.now(),
'total_flows': len(traffic_flows),
'anomalies': [],
'risk_assessment': {}
}
# Build communication graph
comm_graph = self.build_communication_graph(traffic_flows)
# Detect anomalous patterns
anomalies = self.detect_anomalies(comm_graph)
analysis['anomalies'] = anomalies
# Identify lateral movement
lateral_movements = self.detect_lateral_movement(traffic_flows)
if lateral_movements:
analysis['anomalies'].extend(lateral_movements)
# Machine learning analysis
ml_predictions = self.ml_model.predict_threats(traffic_flows)
analysis['ml_threats'] = ml_predictions
# Risk assessment
analysis['risk_assessment'] = self.assess_overall_risk(analysis)
return analysis
def detect_lateral_movement(self, flows):
"""Detect lateral movement patterns"""
lateral_patterns = []
# Group by time windows
time_windows = self.group_by_time_window(flows, window_size=300)
for window, window_flows in time_windows.items():
# Build movement chains
chains = self.build_movement_chains(window_flows)
for chain in chains:
if len(chain) >= 3: # Multi-hop movement
pattern = {
'type': 'lateral_movement_chain',
'hops': len(chain),
'path': [node['ip'] for node in chain],
'services': [node['service'] for node in chain],
'confidence': self.calculate_chain_confidence(chain),
'risk_score': self.calculate_chain_risk(chain)
}
lateral_patterns.append(pattern)
return lateral_patterns
def build_movement_chains(self, flows):
"""Build potential lateral movement chains"""
chains = []
# Create directed graph
G = nx.DiGraph()
for flow in flows:
G.add_edge(
flow['source_ip'],
flow['dest_ip'],
service=flow['dest_port'],
timestamp=flow['timestamp']
)
# Find paths indicating lateral movement
for source in G.nodes():
for target in G.nodes():
if source != target:
try:
paths = list(nx.all_simple_paths(G, source, target, cutoff=5))
for path in paths:
if self.is_lateral_movement_path(path, G):
chains.append(self.build_chain_details(path, G))
except nx.NetworkXNoPath:
continue
return chains
Policy Enforcement Verification
Zero Trust Policy Engine
class ZeroTrustPolicyEngine:
def __init__(self):
self.policies = self.load_policies()
self.enforcement_monitor = EnforcementMonitor()
self.violation_handler = ViolationHandler()
def verify_policy_compliance(self, event):
"""Verify event against Zero Trust policies"""
verification_result = {
'compliant': True,
'violations': [],
'policy_matches': [],
'enforcement_actions': []
}
# Check each policy
for policy in self.policies:
if self.policy_applies(policy, event):
result = self.evaluate_policy(policy, event)
if not result['compliant']:
verification_result['compliant'] = False
verification_result['violations'].append({
'policy_id': policy['id'],
'policy_name': policy['name'],
'violation_details': result['details'],
'severity': policy['severity']
})
# Determine enforcement action
action = self.determine_enforcement_action(policy, result)
verification_result['enforcement_actions'].append(action)
else:
verification_result['policy_matches'].append(policy['id'])
# Execute enforcement actions
if verification_result['enforcement_actions']:
self.execute_enforcement_actions(
verification_result['enforcement_actions'],
event
)
return verification_result
def evaluate_policy(self, policy, event):
"""Evaluate specific policy against event"""
evaluation = {
'compliant': True,
'details': {}
}
# Evaluate conditions
for condition in policy['conditions']:
condition_result = self.evaluate_condition(condition, event)
if not condition_result['met']:
evaluation['compliant'] = False
evaluation['details'][condition['name']] = condition_result['reason']
return evaluation
Automated Policy Enforcement
<!-- Zero Trust Policy Enforcement Rules -->
<ossec_config>
<active-response>
<!-- Network Isolation -->
<command>isolate-violator</command>
<location>local</location>
<rules_id>800001,800002,800004</rules_id>
<timeout>0</timeout>
</active-response>
<!-- Identity Lockdown -->
<command>disable-identity</command>
<location>local</location>
<rules_id>800011,800012</rules_id>
<timeout>0</timeout>
</active-response>
<!-- Micro-segment Enforcement -->
<active-response>
<command>enforce-segmentation</command>
<location>server</location>
<rules_id>800020,800021,800022</rules_id>
</active-response>
</ossec_config>
Encryption Verification
Encryption Status Monitoring
<!-- Encryption Verification Rules -->
<group name="zero_trust,encryption">
<!-- Unencrypted Data Transfer -->
<rule id="800030" level="12">
<if_sid>86001</if_sid>
<field name="network.protocol">HTTP</field>
<field name="data.sensitive">true</field>
<description>Zero Trust Violation: Sensitive data over unencrypted channel</description>
<group>zero_trust,encryption_violation</group>
<mitre>
<id>T1040</id>
</mitre>
</rule>
<!-- Weak Encryption Algorithm -->
<rule id="800031" level="11">
<if_sid>86001</if_sid>
<field name="tls.version" type="pcre2">^(1\.0|1\.1)$</field>
<description>Zero Trust Warning: Weak TLS version in use</description>
<group>zero_trust,weak_encryption</group>
</rule>
<!-- Certificate Validation Failure -->
<rule id="800032" level="13">
<if_sid>86001</if_sid>
<field name="tls.certificate_valid">false</field>
<description>Zero Trust Alert: Invalid certificate in use</description>
<group>zero_trust,certificate_issue</group>
</rule>
</group>
Real-Time Risk Scoring
Dynamic Risk Assessment
class ZeroTrustRiskScorer:
def __init__(self):
self.risk_factors = {
'network_segmentation': 0.25,
'identity_verification': 0.3,
'device_compliance': 0.2,
'encryption_status': 0.15,
'behavioral_analysis': 0.1
}
self.ml_risk_model = MLRiskModel()
def calculate_real_time_risk(self, session_context):
"""Calculate real-time risk score for session"""
risk_components = {}
# Network segmentation risk
seg_risk = self.assess_segmentation_risk(session_context['network'])
risk_components['segmentation'] = seg_risk
# Identity risk
id_risk = self.assess_identity_risk(session_context['identity'])
risk_components['identity'] = id_risk
# Device risk
device_risk = self.assess_device_risk(session_context['device'])
risk_components['device'] = device_risk
# Encryption risk
enc_risk = self.assess_encryption_risk(session_context['encryption'])
risk_components['encryption'] = enc_risk
# Behavioral risk
behav_risk = self.assess_behavioral_risk(session_context['behavior'])
risk_components['behavior'] = behav_risk
# Calculate weighted risk score
total_risk = sum(
risk_components[key] * self.risk_factors[key]
for key in risk_components
)
# ML enhancement
ml_adjustment = self.ml_risk_model.predict_risk_adjustment(
session_context,
risk_components
)
final_risk = min(1.0, total_risk * ml_adjustment)
return {
'risk_score': final_risk,
'risk_level': self.categorize_risk(final_risk),
'components': risk_components,
'recommendations': self.generate_risk_recommendations(
final_risk,
risk_components
)
}
Zero Trust Dashboard Integration
Real-Time Monitoring Dashboard
class ZeroTrustDashboard:
def __init__(self, elasticsearch_client):
self.es = elasticsearch_client
self.visualizations = {
'trust_scores': self.trust_score_visualization,
'segmentation_map': self.segmentation_map_visualization,
'policy_compliance': self.policy_compliance_visualization,
'risk_heatmap': self.risk_heatmap_visualization
}
def generate_dashboard_data(self):
"""Generate Zero Trust dashboard data"""
dashboard_data = {
'timestamp': datetime.now(),
'metrics': {},
'visualizations': {},
'alerts': []
}
# Trust score metrics
dashboard_data['metrics']['avg_trust_score'] = self.calculate_avg_trust_score()
dashboard_data['metrics']['high_risk_sessions'] = self.count_high_risk_sessions()
dashboard_data['metrics']['policy_violations'] = self.count_policy_violations()
# Generate visualizations
for viz_name, viz_func in self.visualizations.items():
dashboard_data['visualizations'][viz_name] = viz_func()
# Active alerts
dashboard_data['alerts'] = self.get_active_alerts()
return dashboard_data
def segmentation_map_visualization(self):
"""Generate network segmentation visualization data"""
query = {
"size": 0,
"aggs": {
"segments": {
"terms": {
"field": "network.source_zone",
"size": 50
},
"aggs": {
"destinations": {
"terms": {
"field": "network.dest_zone",
"size": 50
},
"aggs": {
"flow_count": {
"cardinality": {
"field": "flow.id"
}
},
"violations": {
"filter": {
"term": {
"zero_trust.violation": True
}
}
}
}
}
}
}
}
}
result = self.es.search(index="wazuh-zt-*", body=query)
# Process into visualization format
nodes = set()
links = []
for source_bucket in result['aggregations']['segments']['buckets']:
source = source_bucket['key']
nodes.add(source)
for dest_bucket in source_bucket['destinations']['buckets']:
dest = dest_bucket['key']
nodes.add(dest)
links.append({
'source': source,
'target': dest,
'value': dest_bucket['flow_count']['value'],
'violations': dest_bucket['violations']['doc_count']
})
return {
'nodes': list(nodes),
'links': links
}
Integration with Cloud Environments
Cloud-Native Zero Trust
class CloudZeroTrustIntegration:
def __init__(self):
self.cloud_providers = {
'aws': AWSZeroTrust(),
'azure': AzureZeroTrust(),
'gcp': GCPZeroTrust()
}
def verify_cloud_zero_trust(self, cloud_event):
"""Verify Zero Trust in cloud environments"""
provider = cloud_event['cloud']['provider']
if provider in self.cloud_providers:
return self.cloud_providers[provider].verify(cloud_event)
return {
'error': f'Unsupported cloud provider: {provider}'
}
class AWSZeroTrust:
def verify(self, event):
"""Verify AWS Zero Trust implementation"""
verifications = []
# Verify VPC segmentation
if event['service'] == 'ec2':
vpc_result = self.verify_vpc_segmentation(event)
verifications.append(vpc_result)
# Verify IAM least privilege
if event['service'] == 'iam':
iam_result = self.verify_iam_least_privilege(event)
verifications.append(iam_result)
# Verify encryption
if event.get('encryption'):
enc_result = self.verify_encryption_status(event)
verifications.append(enc_result)
return {
'provider': 'aws',
'verifications': verifications,
'compliant': all(v['compliant'] for v in verifications)
}
Best Practices and Implementation
Zero Trust Maturity Model
class ZeroTrustMaturityAssessment:
def __init__(self):
self.maturity_levels = {
1: 'Traditional',
2: 'Advanced',
3: 'Optimal'
}
self.assessment_criteria = {
'identity': self.assess_identity_maturity,
'device': self.assess_device_maturity,
'network': self.assess_network_maturity,
'application': self.assess_application_maturity,
'data': self.assess_data_maturity
}
def assess_maturity(self, organization_data):
"""Assess Zero Trust maturity level"""
assessment = {
'overall_level': 0,
'pillar_scores': {},
'recommendations': [],
'roadmap': []
}
# Assess each pillar
for pillar, assessor in self.assessment_criteria.items():
score = assessor(organization_data[pillar])
assessment['pillar_scores'][pillar] = score
# Calculate overall maturity
assessment['overall_level'] = self.calculate_overall_maturity(
assessment['pillar_scores']
)
# Generate recommendations
assessment['recommendations'] = self.generate_recommendations(
assessment['pillar_scores']
)
# Create roadmap
assessment['roadmap'] = self.create_maturity_roadmap(
assessment['overall_level'],
assessment['pillar_scores']
)
return assessment
Zero Trust Metrics and ROI
Performance Metrics
{
"zero_trust_metrics": {
"security_improvements": {
"lateral_movement_blocked": "94%",
"unauthorized_access_prevented": "89%",
"data_exfiltration_stopped": "91%",
"breach_dwell_time": "4.2 hours (from 71 days)"
},
"operational_metrics": {
"policy_violations_detected": 15234,
"automated_responses": 12876,
"false_positive_rate": "3.2%",
"mean_time_to_respond": "2.3 minutes"
},
"compliance_metrics": {
"segmentation_compliance": "97.8%",
"encryption_compliance": "99.2%",
"identity_verification_rate": "99.7%",
"audit_readiness": "100%"
},
"business_impact": {
"breach_cost_avoided": "$8.4M",
"productivity_improvement": "23%",
"compliance_penalties_avoided": "$2.1M",
"insurance_premium_reduction": "35%"
}
}
}
Implementation Roadmap
Phase 1: Foundation (Months 1-3)
- Deploy micro-segmentation monitoring
- Implement identity verification rules
- Establish baseline trust scores
- Configure basic policy enforcement
Phase 2: Enhancement (Months 4-6)
- Deploy ML-based anomaly detection
- Implement dynamic risk scoring
- Integrate cloud environments
- Advanced policy automation
Phase 3: Optimization (Months 7-9)
- Fine-tune trust algorithms
- Implement predictive analytics
- Full automation deployment
- Continuous improvement processes
Conclusion
Zero Trust is not a product but a journey—one that requires continuous verification, intelligent correlation, and adaptive security. Wazuh’s powerful correlation engine, combined with micro-segmentation monitoring and identity-based controls, provides the foundation for a robust Zero Trust architecture. The key is not just implementing the technology but fostering a culture of continuous verification and least privilege.
Next Steps
- Assess current network segmentation
- Deploy Zero Trust correlation rules
- Implement trust scoring algorithms
- Configure automated policy enforcement
- Establish continuous monitoring dashboards
Remember: In Zero Trust, paranoia is not a bug—it’s the primary feature. Trust nothing, verify everything, and always assume breach.