N.L.D.S. Security Documentation

Security Overview

The Natural Language Detection System (N.L.D.S.) implements comprehensive security measures to protect user data, ensure system integrity, and maintain compliance with industry standards. As the Tier 0 component of JAEGIS v2.2, N.L.D.S. serves as the primary entry point and therefore implements multiple layers of security controls.

Security Architecture

Defense in Depth Strategy

N.L.D.S. implements a multi-layered security approach:

1. Perimeter Security - Network-level protection and access controls

2. Application Security - Input validation, authentication, and authorization

3. Data Security - Encryption, sanitization, and privacy protection

4. Infrastructure Security - Container security, secrets management

5. Monitoring Security - Real-time threat detection and incident response

Security Zones

graph TB
    subgraph "Internet Zone"
        USER[👤 Users]
        THREAT[⚠️ Threats]
    end
    
    subgraph "DMZ Zone"
        LB[🔄 Load Balancer]
        WAF[🛡️ Web Application Firewall]
        DDOS[🚫 DDoS Protection]
    end
    
    subgraph "Application Zone"
        API[🔌 N.L.D.S. API]
        AUTH[🔐 Authentication Service]
        RATE[⏱️ Rate Limiter]
    end
    
    subgraph "Processing Zone"
        PROC[🔄 Processing Engine]
        ANAL[🧠 Analysis Engine]
        TRANS[🔀 Translation Engine]
    end
    
    subgraph "Data Zone"
        DB[(🗄️ Database)]
        CACHE[(⚡ Cache)]
        SECRETS[🔑 Secrets Store]
    end
    
    subgraph "External Zone"
        OR[🤖 OpenRouter.ai]
        GH[📚 GitHub]
        JAEGIS[🎯 JAEGIS]
    end
    
    USER --> LB
    THREAT -.-> WAF
    LB --> WAF
    WAF --> API
    API --> AUTH
    AUTH --> RATE
    RATE --> PROC
    PROC --> ANAL
    ANAL --> TRANS
    TRANS --> DB
    TRANS --> CACHE
    API --> SECRETS
    TRANS --> OR
    TRANS --> GH
    TRANS --> JAEGIS

Authentication & Authorization

Authentication Methods

JWT Token Authentication

• Algorithm: HS256 with 256-bit secret keys

• Expiration: 1 hour for access tokens, 7 days for refresh tokens

• Claims: User ID, role, permissions, issued/expiry timestamps

• Rotation: Automatic token rotation on refresh

{
  "alg": "HS256",
  "typ": "JWT"
}
{
  "sub": "user123",
  "role": "developer",
  "permissions": ["read", "write", "process"],
  "iat": 1627846261,
  "exp": 1627849861,
  "iss": "nlds-auth-service"
}

API Key Authentication

• Format: nlds_[role]_key_[random] (e.g., nlds_admin_key_a1b2c3d4)

• Encryption: AES-256 encrypted storage

• Scoping: Role-based permissions and rate limits

• Rotation: Configurable rotation policies

Authorization Model

Role-Based Access Control (RBAC)

Role	Permissions	Rate Limit	Use Case
ReadOnly	read	100 req/min	Monitoring, status checks
User	read, write, process	500 req/min	Standard API usage
Developer	read, write, process, debug	1000 req/min	Development, testing
Admin	All permissions	Unlimited	System administration
Service	read, write, process, batch	2000 req/min	Service-to-service

Permission Matrix

Endpoint	ReadOnly	User	Developer	Admin	Service
GET /health	✅	✅	✅	✅	✅
GET /status	❌	✅	✅	✅	✅
POST /process	❌	✅	✅	✅	✅
POST /analyze	❌	✅	✅	✅	✅
POST /translate	❌	✅	✅	✅	✅
POST /batch/*	❌	❌	✅	✅	✅
GET /metrics	❌	❌	✅	✅	❌
POST /admin/*	❌	❌	❌	✅	❌

Input Security

Input Validation

Text Input Validation

class InputValidator:
    MAX_LENGTH = 10000
    MIN_LENGTH = 1
    ALLOWED_CHARS = re.compile(r'^[\w\s\.\,\!\?\-\(\)\[\]\{\}\"\']+$')
    
    def validate_input(self, text: str) -> ValidationResult:
        # Length validation
        if len(text) > self.MAX_LENGTH:
            raise ValidationError(f"Input exceeds maximum length of {self.MAX_LENGTH}")
        
        # Character validation
        if not self.ALLOWED_CHARS.match(text):
            raise ValidationError("Input contains invalid characters")
        
        # Content validation
        if self.contains_malicious_patterns(text):
            raise SecurityError("Input contains potentially malicious content")

Malicious Pattern Detection

• SQL Injection: Detection of SQL injection patterns

• XSS Prevention: HTML/JavaScript sanitization

• Command Injection: System command pattern detection

• Path Traversal: File path manipulation prevention

Content Sanitization

HTML Sanitization

import bleach

def sanitize_html(content: str) -> str:
    allowed_tags = ['p', 'br', 'strong', 'em']
    allowed_attributes = {}
    
    return bleach.clean(
        content,
        tags=allowed_tags,
        attributes=allowed_attributes,
        strip=True
    )

Text Normalization

• Unicode Normalization: NFC normalization for consistent processing

• Whitespace Handling: Trim and normalize whitespace

• Encoding Validation: UTF-8 encoding validation

• Length Limits: Enforce maximum input lengths

Data Protection

Encryption

Data at Rest

• Database: AES-256 encryption for sensitive fields

• File Storage: Encrypted file system (EFS with KMS)

• Backups: Encrypted backup storage with separate keys

• Logs: Encrypted log storage with retention policies

Data in Transit

• TLS 1.3: All external communications

• Certificate Pinning: API client certificate validation

• HSTS: HTTP Strict Transport Security headers

• Perfect Forward Secrecy: Ephemeral key exchange

Data Classification

Classification	Examples	Protection Level
Public	API documentation, status	Standard TLS
Internal	System metrics, logs	TLS + Access Control
Confidential	User inputs, analysis results	TLS + Encryption + Audit
Restricted	API keys, tokens	TLS + Encryption + HSM

Privacy Protection

Data Minimization

• Collect only necessary data for processing

• Automatic data purging after retention period

• Anonymization of analytics data

• Opt-out mechanisms for data collection

PII Handling

class PIIDetector:
    def detect_pii(self, text: str) -> List[PIIMatch]:
        patterns = {
            'email': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
            'phone': r'\b\d{3}-\d{3}-\d{4}\b',
            'ssn': r'\b\d{3}-\d{2}-\d{4}\b',
            'credit_card': r'\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b'
        }
        
        matches = []
        for pii_type, pattern in patterns.items():
            for match in re.finditer(pattern, text):
                matches.append(PIIMatch(
                    type=pii_type,
                    value=match.group(),
                    start=match.start(),
                    end=match.end()
                ))
        
        return matches

Network Security

API Security

Rate Limiting

class RateLimiter:
    def __init__(self):
        self.limits = {
            'readonly': {'requests': 100, 'window': 60},
            'user': {'requests': 500, 'window': 60},
            'developer': {'requests': 1000, 'window': 60},
            'admin': {'requests': -1, 'window': 60},  # Unlimited
            'service': {'requests': 2000, 'window': 60}
        }
    
    def check_rate_limit(self, user_role: str, identifier: str) -> bool:
        limit_config = self.limits.get(user_role)
        if not limit_config or limit_config['requests'] == -1:
            return True
        
        current_count = self.get_request_count(identifier, limit_config['window'])
        return current_count < limit_config['requests']

DDoS Protection

• Rate Limiting: Multi-tier rate limiting (IP, user, endpoint)

• Traffic Analysis: Real-time traffic pattern analysis

• Automatic Blocking: Suspicious IP automatic blocking

• CDN Protection: CloudFlare DDoS protection integration

Network Segmentation

Firewall Rules

# Network Security Groups
ingress_rules:
  - port: 443
    protocol: tcp
    source: 0.0.0.0/0
    description: "HTTPS traffic"
  
  - port: 80
    protocol: tcp
    source: 0.0.0.0/0
    description: "HTTP redirect to HTTPS"

egress_rules:
  - port: 443
    protocol: tcp
    destination: openrouter.ai
    description: "OpenRouter API access"
  
  - port: 443
    protocol: tcp
    destination: api.github.com
    description: "GitHub API access"

Infrastructure Security

Container Security

Image Security

# Security-hardened base image
FROM python:3.9-slim-bullseye

# Create non-root user
RUN groupadd -r nlds && useradd -r -g nlds nlds

# Install security updates
RUN apt-get update && apt-get upgrade -y && \
    apt-get clean && rm -rf /var/lib/apt/lists/*

# Set secure permissions
COPY --chown=nlds:nlds . /app
USER nlds

# Health check
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD curl -f http://localhost:8000/health || exit 1

Runtime Security

• Read-only Root Filesystem: Containers run with read-only root

• No Privileged Containers: All containers run unprivileged

• Resource Limits: CPU and memory limits enforced

• Security Contexts: Non-root user execution

Secrets Management

Kubernetes Secrets

apiVersion: v1
kind: Secret
metadata:
  name: nlds-secrets
type: Opaque
data:
  database-url: <base64-encoded-url>
  jwt-secret: <base64-encoded-secret>
  openrouter-api-key: <base64-encoded-key>

External Secrets Integration

• HashiCorp Vault: Integration for dynamic secrets

• AWS Secrets Manager: Cloud-native secrets storage

• Key Rotation: Automatic key rotation policies

• Audit Logging: All secret access logged

Monitoring & Incident Response

Security Monitoring

Real-time Monitoring

class SecurityMonitor:
    def __init__(self):
        self.threat_patterns = [
            'sql_injection_attempt',
            'xss_attempt',
            'brute_force_attack',
            'unusual_traffic_pattern',
            'privilege_escalation_attempt'
        ]
    
    def analyze_request(self, request: Request) -> SecurityAssessment:
        threats = []
        
        # Check for malicious patterns
        for pattern in self.threat_patterns:
            if self.detect_pattern(request, pattern):
                threats.append(ThreatDetection(
                    type=pattern,
                    severity=self.get_severity(pattern),
                    evidence=self.extract_evidence(request, pattern)
                ))
        
        return SecurityAssessment(
            request_id=request.id,
            threats=threats,
            risk_score=self.calculate_risk_score(threats)
        )

Alerting Framework

• Real-time Alerts: Immediate notification for critical threats

• Escalation Policies: Automated escalation based on severity

• Integration: Slack, PagerDuty, email notifications

• Correlation: Event correlation and pattern analysis

Incident Response

Response Procedures

1. Detection: Automated threat detection and alerting

2. Assessment: Rapid threat assessment and classification

3. Containment: Automatic blocking and isolation

4. Investigation: Forensic analysis and evidence collection

5. Recovery: System restoration and security hardening

6. Lessons Learned: Post-incident review and improvements

Automated Response

class IncidentResponse:
    def handle_security_incident(self, incident: SecurityIncident):
        # Immediate containment
        if incident.severity >= Severity.HIGH:
            self.block_source_ip(incident.source_ip)
            self.revoke_user_tokens(incident.user_id)
        
        # Evidence collection
        self.collect_logs(incident.timeframe)
        self.capture_network_traffic(incident.source_ip)
        
        # Notification
        self.send_alert(incident)
        
        # Escalation
        if incident.severity >= Severity.CRITICAL:
            self.escalate_to_security_team(incident)

Compliance & Auditing

Compliance Standards

SOC 2 Type II

• Security: Comprehensive security controls

• Availability: 99.9% uptime commitment

• Processing Integrity: Data processing accuracy

• Confidentiality: Data protection measures

• Privacy: Privacy control implementation

GDPR Compliance

• Data Subject Rights: Access, rectification, erasure, portability

• Consent Management: Explicit consent mechanisms

• Data Protection Impact Assessment: Regular DPIA reviews

• Breach Notification: 72-hour breach notification procedures

Audit Logging

Comprehensive Logging

class AuditLogger:
    def log_security_event(self, event: SecurityEvent):
        log_entry = {
            'timestamp': datetime.utcnow().isoformat(),
            'event_type': event.type,
            'user_id': event.user_id,
            'source_ip': event.source_ip,
            'user_agent': event.user_agent,
            'endpoint': event.endpoint,
            'method': event.method,
            'status_code': event.status_code,
            'request_id': event.request_id,
            'session_id': event.session_id,
            'risk_score': event.risk_score,
            'additional_data': event.metadata
        }
        
        # Send to secure log aggregation
        self.send_to_siem(log_entry)
        
        # Store in encrypted audit database
        self.store_audit_record(log_entry)

Log Retention

• Security Logs: 7 years retention

• Access Logs: 1 year retention

• Application Logs: 90 days retention

• Debug Logs: 30 days retention

Security Testing

Automated Security Testing

Static Application Security Testing (SAST)

# GitHub Actions Security Scan
name: Security Scan
on: [push, pull_request]

jobs:
  security:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Run Bandit Security Scan
        run: bandit -r nlds/ -f json -o bandit-report.json
      - name: Run Safety Check
        run: safety check --json --output safety-report.json

Dynamic Application Security Testing (DAST)

• OWASP ZAP: Automated vulnerability scanning

• Penetration Testing: Regular third-party security assessments

• Fuzzing: Input fuzzing for vulnerability discovery

• API Security Testing: Comprehensive API security validation

Security Metrics

Key Performance Indicators

• Mean Time to Detection (MTTD): < 5 minutes

• Mean Time to Response (MTTR): < 15 minutes

• False Positive Rate: < 5%

• Security Test Coverage: > 95%

• Vulnerability Remediation Time: < 24 hours (critical), < 7 days (high)

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Security Documentation Version: 1.0 Last Updated: July 26, 2025 Classification: Internal Next Review: August 26, 2025 Security Contact: [e-mail]