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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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このリポジトリの他の Skills
Enterprise database architecture specialist with PostgreSQL 17, MySQL 8.4 LTS,
MongoDB 8.0, Redis 7.4 expertise. Master connection pooling, query optimization,
caching strategies, and database DevOps automation. Build scalable, resilient
database systems with comprehensive monitoring and disaster recovery.
Enterprise Frontend Development with AI-powered modern architecture, Context7 integration, and intelligent component orchestration for scalable user interfaces
Enterprise-grade Python expertise with production patterns for Python 3.13.9, FastAPI 0.115.x, Django 5.2 LTS, Pydantic v2, SQLAlchemy 2.0; activates for API development, ORM usage, async patterns, testing frameworks, and production deployment strategies.
| 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 methodologies
- Secure SDLC: Security-by-design in development lifecycle
- DevSecOps: Security automation and CI/CD integration
- Cloud Security: AWS, Azure, GCP security patterns
- Cryptography: Encryption, hashing, digital signatures
- Identity & 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():
app.before_request(.before_request_handler)
app.after_request(.after_request_handler)
():
._verify_access_control(request)
._prevent_injection_attacks(request)
():
response.headers[] =
response.headers[] =
response.headers[] =
response.headers[] =
response
():
(request, ):
key, value request.form.items():
._detect_sql_injection(value):
SecurityError()
() -> :
patterns = [
,
,
,
]
(re.search(pattern, input_str, re.IGNORECASE) pattern patterns)
flask Flask, request, jsonify
app = Flask(__name__)
security = SecurityMiddleware(app)
():
query =
user = db.execute(query, (user_id,)).fetchone()
user:
jsonify({: }),
jsonify({
: user[],
: user[],
: user[]
})
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 start
- Compliance Ready: Meet regulatory requirements automatically
- Threat Prevention: Identify and mitigate threats early
- Continuous Monitoring: Real-time security posture assessment
For Organizations
- Risk Management: Quantified risk assessment and mitigation
- Audit Trail: Comprehensive security logging and monitoring
- Zero Trust: Never trust, always verify security model
- Scalable 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
