Security Overview

This document provides a comprehensive overview of the security architecture, practices, and procedures implemented in the AI Knowledge Website system.

Table of Contents

1. Security Architecture
2. Authentication and Authorization
3. Data Protection
4. Network Security
5. Application Security
6. Infrastructure Security
7. Monitoring and Incident Response
8. Compliance and Governance
9. Security Policies
10. Threat Model

Security Architecture

Zero-Trust Security Model

The AI Knowledge Website implements a zero-trust security architecture with the following principles:

• Never Trust, Always Verify: All requests are authenticated and authorized
• Principle of Least Privilege: Minimal access rights for users and systems
• Defense in Depth: Multiple layers of security controls
• Assume Breach: Design for containment and rapid response

Security Layers

┌─────────────────────────────────────────────────────────────────┐
│                        Edge Security                            │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐  │
│  │   CDN/WAF       │ │  Rate Limiting  │ │  DDoS Protection│  │
│  └─────────────────┘ └─────────────────┘ └─────────────────┘  │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│                    Application Security                         │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐  │
│  │ Authentication  │ │  Authorization  │ │ Input Validation│  │
│  └─────────────────┘ └─────────────────┘ └─────────────────┘  │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│                     Data Security                               │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐  │
│  │  Encryption     │ │  Data Masking   │ │  Access Logging │  │
│  └─────────────────┘ └─────────────────┘ └─────────────────┘  │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│                 Infrastructure Security                         │
│  ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐  │
│  │ Network Security│ │ Container Security│ │ Secrets Mgmt  │  │
│  └─────────────────┘ └─────────────────┘ └─────────────────┘  │
└─────────────────────────────────────────────────────────────────┘

Authentication and Authorization

API Authentication

JWT Token-Based Authentication:

# Token structure
{
  "sub": "user_id",
  "role": "admin|editor|reader",
  "permissions": ["content:read", "content:write", "pipeline:execute"],
  "exp": 1704067200,
  "iat": 1704063600,
  "iss": "ai-knowledge-api"
}

Token Management:

• Access tokens: 1-hour expiration
• Refresh tokens: 30-day expiration with rotation
• API keys: Long-lived for service-to-service communication
• Token revocation support for compromised credentials

Role-Based Access Control (RBAC)

Role	Permissions	Use Case
Super Admin	All permissions	System administration
Admin	Content, pipeline, config management	Content administrators
Editor	Content creation, editing, publishing	Content creators
Pipeline	Pipeline execution, status monitoring	Automated systems
Reader	Read-only access to published content	Public API consumers

Permission System

permissions:
  content:
    - read: Access to published content
    - write: Create and edit content
    - delete: Remove content items
    - publish: Publish draft content
  
  pipeline:
    - execute: Run pipeline jobs
    - monitor: View pipeline status
    - configure: Modify pipeline settings
  
  admin:
    - users: Manage user accounts
    - system: System configuration
    - security: Security settings

Data Protection

Data Classification

Classification Levels:

1. Public: Published content, API documentation
2. Internal: Processing metadata, system logs
3. Confidential: User data, API keys, configuration
4. Restricted: Security keys, personal identifiers

Encryption Standards

Data at Rest:

• Database: AES-256 encryption with key rotation
• File storage: AES-256 encryption with customer-managed keys
• Backups: Encrypted with separate key hierarchy

Data in Transit:

• TLS 1.3 for all external communications
• mTLS for service-to-service communication
• Certificate pinning for critical connections

Key Management:

• AWS KMS/Google Cloud KMS for key management
• Hardware Security Modules (HSM) for key storage
• Automated key rotation every 90 days
• Separate keys per environment and data type

Data Loss Prevention (DLP)

Content Scanning:

• Automated detection of sensitive data patterns
• PII detection and redaction in logs
• API key detection and alerting
• Credit card and social security number detection

Data Handling Policies:

• No sensitive data in logs or error messages
• Automatic data retention and deletion
• Data minimization principles
• Privacy-by-design architecture

Network Security

Network Architecture

Network Segmentation:

Internet → WAF → Load Balancer → DMZ → Internal Network
                      ↓
               Application Layer
                      ↓
               Database Layer (Private Subnet)

Security Controls:

• Web Application Firewall (WAF) with OWASP rule sets
• Network ACLs and security groups
• VPC isolation with private subnets
• Egress filtering and monitoring

API Security

Rate Limiting:

# Rate limiting tiers
RATE_LIMITS = {
    'public_api': '100/hour',
    'authenticated_api': '1000/hour', 
    'premium_api': '10000/hour',
    'pipeline_api': '100/minute'
}

# Adaptive rate limiting based on behavior
ADAPTIVE_LIMITS = {
    'suspicious_behavior': '10/hour',
    'failed_authentication': '5/hour',
    'known_good_client': '2000/hour'
}

Input Validation:

• Comprehensive input sanitization
• SQL injection prevention
• XSS protection with CSP headers
• File upload restrictions and scanning
• Request size limits and timeout controls

Application Security

Secure Coding Practices

Input Validation:

from pydantic import BaseModel, validator
import bleach

class ContentInput(BaseModel):
    title: str
    content: str
    
    @validator('title')
    def validate_title(cls, v):
        # Sanitize HTML and prevent XSS
        return bleach.clean(v, tags=[], strip=True)[:200]
    
    @validator('content')
    def validate_content(cls, v):
        # Allow safe HTML tags only
        allowed_tags = ['p', 'br', 'strong', 'em', 'ul', 'ol', 'li']
        return bleach.clean(v, tags=allowed_tags, strip=True)

SQL Injection Prevention:

# Good: Parameterized queries
def get_content_by_id(content_id: str) -> Optional[Content]:
    query = """
        SELECT * FROM content_items 
        WHERE id = %s AND status = 'published'
    """
    return session.execute(query, (content_id,)).fetchone()

# Avoid: String concatenation (vulnerable to injection)
# query = f"SELECT * FROM content_items WHERE id = '{content_id}'"

Content Security Policy (CSP)

// CSP Header Configuration
const cspDirectives = {
  'default-src': ["'self'"],
  'script-src': ["'self'", "'unsafe-inline'", 'https://cdnjs.cloudflare.com'],
  'style-src': ["'self'", "'unsafe-inline'", 'https://fonts.googleapis.com'],
  'img-src': ["'self'", 'data:', 'https:'],
  'font-src': ["'self'", 'https://fonts.gstatic.com'],
  'connect-src': ["'self'", 'https://api.ai-knowledge.org'],
  'frame-ancestors': ["'none'"],
  'base-uri': ["'self'"],
  'object-src': ["'none'"]
};

Security Headers

# Standard security headers
Strict-Transport-Security: max-age=31536000; includeSubDomains; preload
X-Content-Type-Options: nosniff
X-Frame-Options: DENY
X-XSS-Protection: 1; mode=block
Referrer-Policy: strict-origin-when-cross-origin
Permissions-Policy: geolocation=(), microphone=(), camera=()

Infrastructure Security

Container Security

Image Security:

# Use minimal base images
FROM python:3.11-slim

# Run as non-root user
RUN groupadd -r appuser && useradd -r -g appuser appuser
USER appuser

# Scan for vulnerabilities
# docker scout cve ai-knowledge/pipeline:latest

Runtime Security:

• Read-only root filesystems
• No privileged containers
• Resource limits and quotas
• Security contexts and pod security policies

Kubernetes Security

Pod Security Standards:

apiVersion: v1
kind: Pod
metadata:
  name: ai-knowledge-pipeline
spec:
  securityContext:
    runAsNonRoot: true
    runAsUser: 1000
    fsGroup: 2000
  containers:
  - name: pipeline
    securityContext:
      allowPrivilegeEscalation: false
      readOnlyRootFilesystem: true
      capabilities:
        drop:
        - ALL

Network Policies:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: ai-knowledge-network-policy
spec:
  podSelector:
    matchLabels:
      app: ai-knowledge
  policyTypes:
  - Ingress
  - Egress
  ingress:
  - from:
    - podSelector:
        matchLabels:
          role: frontend
  egress:
  - to:
    - podSelector:
        matchLabels:
          role: database

Secrets Management

Kubernetes Secrets:

apiVersion: v1
kind: Secret
metadata:
  name: ai-knowledge-secrets
type: Opaque
data:
  database-url: <base64-encoded-value>
  api-key: <base64-encoded-value>

External Secrets Integration:

• AWS Secrets Manager / Google Secret Manager
• HashiCorp Vault integration
• Automatic secret rotation
• Audit trails for secret access

Monitoring and Incident Response

Security Monitoring

Log Monitoring:

# Security event logging
import structlog

security_logger = structlog.get_logger("security")

def log_authentication_attempt(username: str, success: bool, ip_address: str):
    security_logger.info(
        "authentication_attempt",
        username=username,
        success=success,
        ip_address=ip_address,
        event_type="auth",
        severity="medium" if not success else "low"
    )

Metrics and Alerting:

• Failed authentication attempts
• Unusual traffic patterns
• Privilege escalation attempts
• Data exfiltration indicators
• Security policy violations

Intrusion Detection

Application-Level Detection:

• Anomalous API usage patterns
• Suspicious user behavior
• Automated attack signatures
• Business logic violations

Infrastructure-Level Detection:

• Network traffic anomalies
• Process execution monitoring
• File integrity monitoring
• Container runtime security

Compliance and Governance

Regulatory Compliance

GDPR Compliance:

• Data subject rights implementation
• Privacy by design principles
• Data Processing Records (ROPA)
• Data Protection Impact Assessments (DPIA)

SOC 2 Type II:

• Security controls documentation
• Operational effectiveness testing
• Continuous monitoring programs
• Annual compliance audits

Data Governance

Data Retention:

# Automated data retention policies
RETENTION_POLICIES = {
    'user_data': timedelta(days=2555),  # 7 years
    'access_logs': timedelta(days=90),   # 90 days
    'audit_logs': timedelta(days=2555),  # 7 years
    'content_snapshots': timedelta(days=365)  # 1 year
}

Data Subject Rights:

• Right to access personal data
• Right to rectification
• Right to erasure (right to be forgotten)
• Right to data portability
• Right to object to processing

Security Policies

Password Policy

Requirements:

• Minimum 12 characters
• Must include uppercase, lowercase, numbers, symbols
• No common passwords or personal information
• Password rotation every 90 days
• No password reuse (last 12 passwords)

Access Control Policy

Principles:

• Principle of least privilege
• Regular access reviews (quarterly)
• Immediate revocation upon termination
• Multi-factor authentication for all accounts
• Privileged access monitoring

Incident Response Policy

Response Levels:

Level	Response Time	Escalation
Critical	15 minutes	CISO, Legal
High	1 hour	Security Team
Medium	4 hours	DevOps Team
Low	24 hours	Developer

Threat Model

Threat Actors

External Threats:

• Script kiddies: Automated scanning and exploitation
• Criminal organizations: Data theft and ransomware
• Nation-state actors: Advanced persistent threats
• Competitors: Industrial espionage

Internal Threats:

• Malicious insiders: Privilege abuse
• Negligent employees: Accidental exposure
• Compromised accounts: Lateral movement
• Third-party vendors: Supply chain attacks

Attack Vectors

Most Likely Threats:

1. Web Application Attacks (High)

   • SQL injection
   • Cross-site scripting (XSS)
   • Authentication bypass
   • Business logic flaws

2. API Abuse (Medium)

   • Rate limit bypass
   • Data scraping
   • Authentication token theft
   • Parameter pollution

3. Infrastructure Attacks (Medium)

   • Container escape
   • Kubernetes privilege escalation
   • Network lateral movement
   • Credential theft

Risk Assessment

Risk Matrix:

Threat	Likelihood	Impact	Risk Level	Mitigation
Data Breach	Medium	High	High	Encryption, Access Controls
DDoS Attack	High	Medium	High	CDN, Rate Limiting
API Abuse	High	Low	Medium	Authentication, Monitoring
Insider Threat	Low	High	Medium	Access Reviews, Monitoring

Security Testing

Automated Security Testing

Static Application Security Testing (SAST):

# GitHub Actions security scan
- name: Run Bandit Security Check
  run: |
    pip install bandit
    bandit -r pipelines/ -f json -o bandit-report.json

- name: Run Semgrep SAST
  uses: returntocorp/semgrep-action@v1
  with:
    config: >-
      p/security-audit
      p/secrets

Dynamic Application Security Testing (DAST):

• OWASP ZAP integration in CI/CD
• Regular penetration testing
• API security testing
• Container vulnerability scanning

Manual Security Testing

Quarterly Security Reviews:

• Code review for security issues
• Architecture review for security flaws
• Configuration review for misconfigurations
• Access control review for violations

Incident Response Procedures

Immediate Response (0-15 minutes)

1. Detect and Assess

   • Automated alerting triggers
   • Initial severity assessment
   • Stakeholder notification

2. Contain

   • Isolate affected systems
   • Preserve evidence
   • Prevent further damage

Short-term Response (15 minutes - 4 hours)

1. Investigate

   • Root cause analysis
   • Impact assessment
   • Evidence collection

2. Communicate

   • Internal stakeholder updates
   • Customer notification (if required)
   • Regulatory reporting (if required)

Recovery and Lessons Learned (4+ hours)

1. Recover

   • System restoration
   • Service validation
   • Performance monitoring

2. Learn

   • Post-incident review
   • Process improvements
   • Security control updates

---

Security Contacts:

• Security Team: security@ai-knowledge.org
• Incident Response: incidents@ai-knowledge.org
• Emergency: +1-XXX-XXX-XXXX (24/7 security hotline)

Last Updated: January 2024
Classification: Internal
Review Cycle: Quarterly