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moai-domain-security Enterprise-grade security expertise with production-ready patterns for OWASP Top 10 2021, zero-trust architecture, threat modeling (STRIDE, PASTA), secure SDLC, DevSecOps automation, cloud security, cryptography, identity & access management, and compliance frameworks (SOC 2, ISO 27001, GDPR, CCPA).
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Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
name moai-domain-security version 4.0.0 created 2025-10-28 updated 2025-11-12 status stable tier domain description Enterprise-grade security expertise with production-ready patterns for OWASP Top 10 2021, zero-trust architecture, threat modeling (STRIDE, PASTA), secure SDLC, DevSecOps automation, cloud security, cryptography, identity & access management, and compliance frameworks (SOC 2, ISO 27001, GDPR, CCPA). allowed-tools Read, Bash, WebSearch, WebFetch, mcp__context7__resolve-library-id, mcp__context7__get-library-docs primary-agent security-expert secondary-agents ["qa-validator","alfred","doc-syncer"] keywords ["security","owasp","zero-trust","devsecops","threat-modeling","cryptography","compliance","soc2","iso27001","gdpr"]
moai-domain-security — Enterprise Security Architecture
Enterprise Security Expertise & Implementation
Primary Agent : security-expert
Secondary Agents : qa-validator, alfred, doc-syncer
Version : 4.0.0
📖 Progressive Disclosure
Level 1: Quick Reference
Enterprise security expertise with OWASP Top 10 2021 compliance and zero-trust architecture .
Core Capabilities :
Threat Modeling : STRIDE, PASTA methodologiesSecure SDLC : Security-by-design in development lifecycleDevSecOps : Security automation and CI/CD integrationCloud Security : AWS, Azure, GCP security patternsCryptography : Encryption, hashing, digital signaturesIdentity & Access Management : OAuth, JWT, RBAC implementation
When to Use :
✅ Application security assessments and penetration testing
✅ Secure architecture design and threat modeling
✅ DevSecOps pipeline implementation
✅ Compliance frameworks (SOC 2, ISO 27001, GDPR)
✅ Cloud security hardening and monitoring
Level 2: Practical Implementation
Pattern 1: OWASP Top 10 2021 Protection
Objective : Protect against the OWASP Top 10 2021 vulnerabilities.
import re
from functools import wraps
class SecurityMiddleware :
"""OWASP Top 10 protection middleware."""
def __init__ (self, app=None ):
self .app = app
if app:
self .init_app(app)
def init_app (self, app
self
self
def
before_request_handler
self, request
self
self
return
None
def
after_request_handler
self, response
'X-Content-Type-Options'
'nosniff'
'X-Frame-Options'
'DENY'
'X-XSS-Protection'
'1; mode=block'
'Strict-Transport-Security'
'max-age=31536000'
return
def
_prevent_injection_attacks
self, request
if
hasattr
'form'
for
in
if
self
raise
"SQL injection attempt detected"
def
_detect_sql_injection
self, input_str: str
bool
r"(\b(union|select|insert|update|delete|drop)\b)"
r"([';]|--|/\*|\*/|xp_|sp_)"
r"(or\s+1\s*=\s*1|and\s+1\s*=\s*1)"
return
any
for
in
from
import
@app.route('/api/users/<int:user_id>'
def
get_user
user_id
"SELECT id, username, email FROM users WHERE id = %s"
if
not
return
'error'
'User not found'
404
return
'id'
'id'
'username'
'username'
'email'
'email'
Pattern 2: Zero-Trust Architecture Objective : Implement zero-trust security principles.
import jwt
import secrets
from datetime import datetime, timedelta
class ZeroTrustAuth :
"""Zero-trust authentication and authorization system."""
def __init__ (self, secret_key: str , token_expiry: int = 3600 ):
self .secret_key = secret_key
self .token_expiry = token_expiry
self .active_sessions = {}
def authenticate_user (self, credentials: dict , context: dict ) -> dict :
user = self ._verify_credentials(credentials)
if not user:
raise AuthenticationError("Invalid credentials" )
risk_score = self ._calculate_risk_score(user, context)
trust_level = self ._determine_trust_level(risk_score)
token_claims = {
'user_id' : user['id' ],
'username' : user['username' ],
'roles' : user['roles' ],
'trust_level' : trust_level,
'session_id' : secrets.token_urlsafe(32 ),
'device_fingerprint' : context.get('device_fingerprint' ),
'ip_address' : context.get('ip_address' ),
'risk_score' : risk_score,
'exp' : datetime.utcnow() + timedelta(seconds=self .token_expiry)
}
token = jwt.encode(token_claims, self .secret_key, algorithm='HS256' )
self .active_sessions[token_claims['session_id' ]] = {
'user_id' : user['id' ],
'created_at' : datetime.utcnow(),
'context' : context,
'risk_score' : risk_score
}
return {
'token' : token,
'session_id' : token_claims['session_id' ],
'trust_level' : trust_level,
'expires_in' : self .token_expiry
}
def _calculate_risk_score (self, user: dict , context: dict ) -> int :
risk_score = 0
if self ._is_unusual_location(user['id' ], context.get('ip_address' )):
risk_score += 20
if self ._is_new_device(user['id' ], context.get('device_fingerprint' )):
risk_score += 15
if self ._is_unusual_time(user['id' ]):
risk_score += 10
if self ._is_unusual_behavior(user['id' ], context):
risk_score += 25
return min (risk_score, 100 )
def _determine_trust_level (self, risk_score: int ) -> str :
if risk_score < 20 :
return 'high'
elif risk_score < 50 :
return 'medium'
else :
return 'low'
def zero_trust_required (f ):
@wraps(f )
def decorated_function (*args, **kwargs ):
token = request.headers.get('Authorization' )
if not token:
return jsonify({'error' : 'Authorization required' }), 401
try :
auth_result = zero_trust.verify_request(
token=token,
request_context={
'ip_address' : request.remote_addr,
'device_fingerprint' : request.headers.get('User-Agent' ),
'endpoint' : request.endpoint,
'method' : request.method
}
)
request.auth_result = auth_result
return f(*args, **kwargs)
except AuthorizationError as e:
return jsonify({'error' : str (e)}), 401
return decorated_function
@app.route('/api/sensitive-data'
@zero_trust_required
def get_sensitive_data ():
if 'read_sensitive_data' not in request.auth_result['permissions' ]:
return jsonify({'error' : 'Insufficient permissions' }), 403
sensitive_data = get_data_for_user(request.auth_result['user_id' ])
audit_log.info(
"Sensitive data accessed" ,
user_id=request.auth_result['user_id' ],
trust_level=request.auth_result['trust_level' ],
risk_score=request.auth_result['risk_score' ]
)
return jsonify(sensitive_data)
Pattern 3: Threat Modeling (STRIDE) Objective : Implement STRIDE threat modeling for system security.
from enum import Enum
from dataclasses import dataclass
from typing import List , Dict
class ThreatCategory (Enum ):
SPOOFING = "Spoofing"
TAMPERING = "Tampering"
REPUDIATION = "Repudiation"
INFORMATION_DISCLOSURE = "Information Disclosure"
DENIAL_OF_SERVICE = "Denial of Service"
ELEVATION_OF_PRIVILEGE = "Elevation of Privilege"
@dataclass
class Threat :
category: ThreatCategory
description: str
impact: str
likelihood: str
mitigation: List [str ]
affected_components: List [str ]
class ThreatModelAnalyzer :
"""STRIDE threat modeling analyzer."""
def analyze_system (self, system_architecture: Dict ) -> List [Threat]:
threats = []
for component_name, component_config in system_architecture.items():
component_threats = self ._analyze_component(component_name, component_config)
threats.extend(component_threats)
return threats
def _analyze_component (self, component_name: str , component_config: Dict ) -> List [Threat]:
threats = []
component_type = component_config.get('type' , '' )
if component_type == 'web_application' :
threats.extend([
Threat(
category=ThreatCategory.SPOOFING,
description="Attacker impersonates legitimate user" ,
impact="High" ,
likelihood="Medium" ,
mitigation=[
"Implement strong authentication (MFA)" ,
"Use CSRF tokens" ,
"Implement proper session management"
],
affected_components=[component_name]
),
Threat(
category=ThreatCategory.INFORMATION_DISCLOSURE,
description="Sensitive data exposed through vulnerabilities" ,
impact="High" ,
likelihood="High" ,
mitigation=[
"Encrypt data at rest and in transit" ,
"Implement proper access controls" ,
"Use secure coding practices"
],
affected_components=[component_name]
)
])
return threats
def generate_threat_report (self, threats: List [Threat] ) -> Dict :
threats_by_category = {}
for threat in threats:
category = threat.category.value
if category not in threats_by_category:
threats_by_category[category] = []
threats_by_category[category].append(threat)
high_risk_threats = [
threat for threat in threats
if threat.impact == "High" and threat.likelihood in ["High" , "Medium" ]
]
return {
'total_threats' : len (threats),
'threats_by_category' : threats_by_category,
'high_risk_threats' : len (high_risk_threats),
'recommendations' : self ._generate_recommendations(threats)
}
def _generate_recommendations (self, threats: List [Threat] ) -> List [str ]:
recommendations = []
mitigations = set ()
for threat in threats:
mitigations.update(threat.mitigation)
priority_mitigations = [
"Implement strong authentication (MFA)" ,
"Encrypt data at rest and in transit" ,
"Use parameterized queries" ,
"Implement proper access controls" ,
"Use secure coding practices"
]
for mitigation in priority_mitigations:
if mitigation in mitigations:
recommendations.append(mitigation)
mitigations.remove(mitigation)
recommendations.extend(sorted (mitigations))
return recommendations
system_architecture = {
'web_application' : {
'type' : 'web_application' ,
'technologies' : ['React' , 'Node.js' , 'Express' ],
'exposed' : True
},
'api' : {
'type' : 'api' ,
'technologies' : ['FastAPI' , 'Python' ],
'exposed' : True
},
'database' : {
'type' : 'database' ,
'technologies' : ['PostgreSQL' ],
'exposed' : False
}
}
analyzer = ThreatModelAnalyzer()
threats = analyzer.analyze_system(system_architecture)
report = analyzer.generate_threat_report(threats)
print (f"Threat Analysis Report" )
print (f"Total threats: {report['total_threats' ]} " )
print (f"High-risk threats: {report['high_risk_threats' ]} " )
print (f"Top recommendations: {report['recommendations' ][:3 ]} " )
Level 3: Advanced Integration
DevSecOps Pipeline Integration Security automation in CI/CD pipeline :
name: Security Pipeline
on:
push:
branches: [ main , develop ]
pull_request:
branches: [ main ]
jobs:
security-scan:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Run security scan
run: |
# OWASP ZAP Baseline Scan
docker run -t owasp/zap2docker-stable zap-baseline.py -t http://app-url
pip install safety
safety check --json --output safety-report.json
pip install bandit
bandit -r src/ -f json -o bandit-report.json
- name: Threat modeling
run: |
python threat_modeling.py --architecture architecture.json --output threat-model.json
- name: Compliance check
run: |
python compliance_check.py --framework soc2
python compliance_check.py --framework gdpr
🔗 Integration with Alfred Workflow
Command Integration
Use: Security expert agent for threat modeling
Tools: STRIDE analysis, vulnerability scanning
Use: QA validation agent for compliance checks
Tools: SOC 2, ISO 27001, GDPR validation
Skill Dependencies
moai-domain-cloud: Cloud security patternsmoai-alfred-dev-guide: Secure development practicesmoai-alfred-best-practices: Security best practices
📚 Key Benefits
For Development Teams
Proactive Security : Build security in from the startCompliance Ready : Meet regulatory requirements automaticallyThreat Prevention : Identify and mitigate threats earlyContinuous Monitoring : Real-time security posture assessment
For Organizations
Risk Management : Quantified risk assessment and mitigationAudit Trail : Comprehensive security logging and monitoringZero Trust : Never trust, always verify security modelScalable Security : Security that grows with your organization
📚 Research Attribution Security Research : Based on OWASP Top 10 2021, NIST Cybersecurity Framework, and zero-trust architecture principles
Compliance Frameworks : SOC 2, ISO 27001, GDPR, CCPA implementation patterns
🔗 Related Resources Complete Security Patterns : See examples.md
Compliance Checklists : See reference.md
Threat Modeling Templates : See examples.md
