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Security

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Security Guide

This guide covers security considerations and best practices for deploying and using the Cognitive Engine.

Security Overview
API Key Management
Data Security
Network Security
Application Security
Agent Security
Memory Security
Deployment Security
Compliance
Security Auditing

Security Overview

The Cognitive Engine processes potentially sensitive information and interacts with external APIs. Security is critical for:

Protecting API keys and credentials
Securing user data and queries
Preventing unauthorized access
Ensuring safe agent behavior
Maintaining data privacy
Complying with regulations

Security Principles

Least Privilege: Components have only necessary access
Defense in Depth: Multiple layers of security
Secure by Default: Safe configurations out of the box
Transparency: Security practices are documented
Continuous Improvement: Regular security updates

API Key Management

Storing API Keys

Never commit API keys to version control

❌ Wrong:

# .env file committed to git
OPENAI_API_KEY=sk-actual-key-here

✅ Correct:

# .env.example with placeholders
OPENAI_API_KEY=your_openai_api_key_here

# .env file in .gitignore
OPENAI_API_KEY=sk-actual-key-here

Using Environment Variables

# Set environment variables
export OPENAI_API_KEY="your-key"
export ANTHROPIC_API_KEY="your-key"

# Or use .env file
echo "OPENAI_API_KEY=your-key" > .env

Using Secrets Managers

For production deployments, use a secrets manager:

AWS Secrets Manager

import boto3

def get_secret(secret_name):
    client = boto3.client('secretsmanager')
    response = client.get_secret_value(SecretId=secret_name)
    return response['SecretString']

api_key = get_secret('cognitive-engine/openai-api-key')

HashiCorp Vault

import hvac

client = hvac.Client(url='https://vault.example.com')
client.auth.approle.login(role_id='your-role', secret_id='your-secret')
api_key = client.read('secret/cognitive-engine/openai')['data']['value']

Environment Variable Injection

# Kubernetes Secret
apiVersion: v1
kind: Secret
metadata:
  name: api-keys
type: Opaque
stringData:
  openai-key: your-key
  anthropic-key: your-key

Key Rotation

Regularly rotate API keys:

Generate new API key from provider
Update application configuration
Test with new key
Revoke old key
Monitor for issues

:::bash

Rotation script

!/bin/bash

Generate new key (manual step from provider)

NEW_KEY="new-key-here"

Update configuration

sed -i "s/OPENAI_API_KEY=.*/OPENAI_API_KEY=$NEW_KEY/" .env

Restart application

systemctl restart cognitive-engine

Test

python run.py test

If successful, revoke old key (manual step from provider)

Key Scoping

Use scoped keys when possible:

# OpenAI allows scoped keys
# Create key with specific permissions only
# - Read-only access
# - Specific models only
# - Rate limits

Data Security

Data at Rest

Encrypt sensitive data stored on disk:

from cryptography.fernet import Fernet
import os

class SecureStorage:
    def __init__(self, key=None):
        if key is None:
            key = os.environ.get('ENCRYPTION_KEY')
            if not key:
                key = Fernet.generate_key()
        self.cipher = Fernet(key)

    def encrypt(self, data):
        return self.cipher.encrypt(data.encode())

    def decrypt(self, encrypted_data):
        return self.cipher.decrypt(encrypted_data).decode()

# Use for sensitive memory entries
storage = SecureStorage()
encrypted = storage.encrypt("sensitive data")

Database Encryption

For SQLite, use SQLCipher or encrypt the entire file:

# Encrypt database file
openssl enc -aes-256-cbc -salt -in cognitive_engine.db -out cognitive_engine.db.enc

# Decrypt when needed
openssl enc -aes-256-cbc -d -in cognitive_engine.db.enc -out cognitive_engine.db

For production, use PostgreSQL with transparent data encryption (TDE).

Data in Transit

Always use HTTPS/TLS:

# Force HTTPS in web applications
from flask import Flask
from flask_talisman import Talisman

app = Flask(__name__)
Talisman(app, force_https=True)

Input Validation

Validate all user inputs:

from pydantic import BaseModel, validator
import re

class QueryInput(BaseModel):
    query: str

    @validator('query')
    def validate_query(cls, v):
        # Length check
        if len(v) > 10000:
            raise ValueError("Query too long")

        # Content check
        if not re.match(r'^[\w\s\.,!?;:()-]+$', v):
            raise ValueError("Invalid characters")

        # SQL injection prevention
        dangerous = ['DROP', 'DELETE', 'TRUNCATE', 'ALTER']
        if any(word in v.upper() for word in dangerous):
            raise ValueError("Potentially dangerous query")

        return v

Output Sanitization

Sanitize outputs from LLMs:

import html

def sanitize_output(text):
    """Sanitize LLM output to prevent XSS."""
    return html.escape(text)

# Or use a library
from bleach import clean

sanitized = clean(text, tags=[], attributes={})

Data Retention

Implement data retention policies:

# Automatic cleanup script
#!/bin/bash
# Delete data older than 90 days
find /path/to/memory -name "*.db" -mtime +90 -delete
find /path/to/logs -name "*.log" -mtime +30 -delete

:::python
# In application
from datetime import datetime, timedelta

def cleanup_old_memory():
    cutoff = datetime.now() - timedelta(days=90)
    memory.db.query(EpisodicMemory).filter(
        EpisodicMemory.created_at < cutoff
    ).delete()

Network Security

Firewall Configuration

Configure firewalls to restrict access:

# UFW (Ubuntu)
sudo ufw default deny incoming
sudo ufw default allow outgoing
sudo ufw allow ssh
sudo ufw allow 8000/tcp  # Dashboard port
sudo ufw enable

:::yaml
# AWS Security Group
Resources:
  SecurityGroup:
    Type: AWS::EC2::SecurityGroup
    Properties:
      GroupDescription: Cognitive Engine Security Group
      SecurityGroupIngress:

        - IpProtocol: tcp
          FromPort: 22
          ToPort: 22
          CidrIp: your.ip.address/32
        - IpProtocol: tcp
          FromPort: 8000
          ToPort: 8000
          CidrIp: your.network/24

TLS/SSL Configuration

Use valid SSL certificates:

# FastAPI with HTTPS
from fastapi import FastAPI
import uvicorn

app = FastAPI()

if __name__ == "__main__":
    uvicorn.run(
        app,
        host="0.0.0.0",
        port=8000,
        ssl_keyfile="path/to/key.pem",
        ssl_certfile="path/to/cert.pem"
    )

Rate Limiting

Implement rate limiting to prevent abuse:

from slowapi import Limiter, _rate_limit_exceeded_handler
from slowapi.util import get_remote_address
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse

limiter = Limiter(key_func=get_remote_address)
app = FastAPI()
app.state.limiter = limiter
app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)

@app.post("/query")
@limiter.limit("10/minute")
async def query(request: Request):
    return {"result": "response"}

VPN/Private Networks

Deploy in private networks:

# VPC configuration
Resources:
  VPC:
    Type: AWS::EC2::VPC
    Properties:
      CidrBlock: 10.0.0.0/16

  PrivateSubnet:
    Type: AWS::EC2::Subnet
    Properties:
      VpcId: !Ref VPC
      CidrBlock: 10.0.1.0/24

Application Security

Authentication

Implement authentication for web interfaces:

from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials

app = FastAPI()
security = HTTPBearer()

def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
    token = credentials.credentials
    if token != os.environ.get('API_TOKEN'):
        raise HTTPException(status_code=403, detail="Invalid token")
    return token

@app.post("/query")
async def query(token: str = Depends(verify_token)):
    return {"result": "response"}

Authorization

Implement role-based access control:

from enum import Enum

class Role(Enum):
    ADMIN = "admin"
    USER = "user"
    READONLY = "readonly"

def check_permission(required_role: Role):
    def decorator(func):
        def wrapper(user, *args, **kwargs):
            if user.role != required_role and user.role != Role.ADMIN:
                raise HTTPException(status_code=403, detail="Insufficient permissions")
            return func(user, *args, **kwargs)
        return wrapper
    return decorator

Secure Dependencies

Keep dependencies updated:

# Check for vulnerabilities
pip install safety
safety check

# Update dependencies
pip install --upgrade -r requirements.txt

# Use pip-audit
pip install pip-audit
pip-audit

Use a requirements file with pinned versions:

# requirements.txt
openai==1.3.0
anthropic==0.7.0
pydantic==2.0.0
fastapi==0.100.0

Code Security

Use static analysis tools:

# Bandit - Python security linter
pip install bandit
bandit -r cognitive_engine/

# Semgrep - semantic code analysis
pip install semgrep
semgrep --config=auto cognitive_engine/

Logging Security

Don't log sensitive information:

import logging

logger = logging.getLogger(__name__)

def process_query(query):
    # Don't log the full query if it contains sensitive data
    safe_query = sanitize_for_logging(query)
    logger.info(f"Processing query: {safe_query[:50]}...")

Agent Security

Goal Validation

Validate agent goals to prevent dangerous actions:

class GoalValidator:
    DANGEROUS_KEYWORDS = ['delete', 'destroy', 'format', 'erase', 'hack']

    def validate(self, goal: str) -> bool:
        goal_lower = goal.lower()

        # Check for dangerous keywords
        for keyword in self.DANGEROUS_KEYWORDS:
            if keyword in goal_lower:
                return False

        # Check for system commands
        if goal_lower.startswith(('sudo', 'rm ', 'dd ')):
            return False

        # Check for external access attempts
        if 'ssh' in goal_lower or 'ftp' in goal_lower:
            return False

        return True

validator = GoalValidator()
if not validator.validate(user_goal):
    raise ValueError("Goal rejected for security reasons")

Tool Restrictions

Restrict which tools agents can use:

class ToolRegistry:
    SAFE_TOOLS = ['web_search', 'code_exec']
    DANGEROUS_TOOLS = ['file_delete', 'system_command']

    def get_tool(self, tool_name: str, user_role: str):
        if user_role != 'admin' and tool_name in self.DANGEROUS_TOOLS:
            raise PermissionError(f"Tool {tool_name} not allowed for role {user_role}")

        if tool_name not in self.SAFE_TOOLS + self.DANGEROUS_TOOLS:
            raise ValueError(f"Unknown tool: {tool_name}")

        return self.tools[tool_name]

Sandbox Execution

Execute code in sandboxed environment:

import docker

class SandboxedExecutor:
    def execute_code(self, code: str):
        client = docker.from_env()

        # Run in isolated container
        container = client.containers.run(
            image='python:3.10-slim',
            command=['python', '-c', code],
            network_disabled=True,
            mem_limit='128m',
            cpu_quota=50000,
            runtime='runsc',  # gVisor for additional isolation
            remove=True,
            stdout=True,
            stderr=True
        )

        return container.decode('utf-8')

Step Limits

Enforce step limits to prevent infinite loops:

class Agent:
    def __init__(self, max_steps=100):
        self.max_steps = max_steps
        self.step_count = 0

    def run(self, goal: str):
        while not self.goal_achieved and self.step_count < self.max_steps:
            self.step_count += 1
            self.execute_step()

        if self.step_count >= self.max_steps:
            raise RuntimeError("Maximum steps exceeded")

Memory Control

Control agent memory to prevent accumulation:

class AgentMemory:
    MAX_MEMORY_ENTRIES = 1000

    def add_entry(self, entry):
        if len(self.memory) >= self.MAX_MEMORY_ENTRIES:
            # Remove oldest entries
            self.memory = self.memory[-(self.MAX_MEMORY_ENTRIES // 2):]

        self.memory.append(entry)

Memory Security

Memory Encryption

Encrypt sensitive memory entries:

from cryptography.fernet import Fernet

class SecureMemory:
    def __init__(self, encryption_key):
        self.cipher = Fernet(encryption_key)

    def store(self, key, value):
        if self.is_sensitive(value):
            encrypted = self.cipher.encrypt(value.encode())
            self.db[key] = encrypted
        else:
            self.db[key] = value

    def retrieve(self, key):
        value = self.db[key]
        if isinstance(value, bytes):
            return self.cipher.decrypt(value).decode()
        return value

    def is_sensitive(self, value):
        # Check for PII, passwords, etc.
        sensitive_patterns = [
            r'\b\d{3}-\d{2}-\d{4}\b',  # SSN
            r'\b\d{16}\b',  # Credit card
            r'password',
            r'token'
        ]
        return any(re.search(pattern, value, re.I) for pattern in sensitive_patterns)

Memory Access Control

Control who can access memory:

class MemoryAccessControl:
    def __init__(self):
        self.acl = {}  # access control list

    def grant_access(self, user_id, permissions):
        self.acl[user_id] = permissions

    def check_access(self, user_id, operation, data):
        if user_id not in self.acl:
            return False

        permissions = self.acl[user_id]
        if operation not in permissions:
            return False

        # Check data-level permissions
        if data.get('sensitivity', 'public') == 'confidential':
            return 'read_confidential' in permissions

        return True

Memory Purging

Implement secure memory deletion:

import hashlib
import os

def secure_delete(file_path):
    """Securely delete file by overwriting."""
    file_size = os.path.getsize(file_path)

    # Overwrite with random data multiple times
    with open(file_path, 'wb') as f:
        for _ in range(3):
            f.write(os.urandom(file_size))
            f.flush()
            os.fsync(f.fileno())

    # Delete file
    os.remove(file_path)

Deployment Security

Container Security

Secure Docker containers:

# Use minimal base image
FROM python:3.10-slim

# Run as non-root user
RUN useradd -m -u 1000 cognitive_engine
USER cognitive_engine

# Use read-only filesystem
# Add --read-only flag to docker run

# Drop capabilities
# Add --cap-drop=ALL --cap-add=NET_BIND_SERVICE to docker run

# Use security options
# docker run --security-opt=no-new-privileges

Kubernetes Security

Configure pod security:

apiVersion: v1
kind: Pod
metadata:
  name: cognitive-engine
spec:
  securityContext:
    runAsNonRoot: true
    runAsUser: 1000
    fsGroup: 1000
  containers:

  - name: cognitive-engine
    image: cognitive-engine:latest
    securityContext:
      allowPrivilegeEscalation: false
      readOnlyRootFilesystem: true
      capabilities:
        drop:
        - ALL

Infrastructure Security

Use secure infrastructure configurations:

# AWS IAM policy with least privilege
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:PutLogEvents"
      ],
      "Resource": "arn:aws:logs:*:*:*"
    }
  ]
}

Secrets Rotation

Automate secret rotation:

import boto3
import time

class SecretRotator:
    def __init__(self, secret_name):
        self.client = boto3.client('secretsmanager')
        self.secret_name = secret_name

    def rotate_secret(self):
        # Generate new secret
        new_secret = self.generate_new_secret()

        # Update secret
        self.client.update_secret(
            SecretId=self.secret_name,
            SecretString=new_secret
        )

        # Wait for propagation
        time.sleep(30)

        # Test new secret
        if not self.test_secret(new_secret):
            # Rollback
            self.rollback_secret()
            raise RuntimeError("Secret rotation failed")

Compliance

For EU data protection:

class GDPRCompliance:
    def __init__(self):
        self.consent_manager = ConsentManager()

    def process_user_data(self, user_id, data):
        # Check consent
        if not self.consent_manager.has_consent(user_id):
            raise PermissionError("No consent for data processing")

        # Process with data minimization
        minimized_data = self.minimize_data(data)

        return self.process(minimized_data)

    def right_to_erasure(self, user_id):
        # Delete all user data
        self.delete_user_data(user_id)
        self.delete_user_memory(user_id)
        self.delete_user_logs(user_id)

HIPAA Compliance

For healthcare data:

class HIPAACompliance:
    def __init__(self):
        self.audit_logger = AuditLogger()

    def process_phi(self, data):
        # Log all access
        self.audit_logger.log_access(data)

        # Encrypt PHI
        encrypted = self.encrypt_phi(data)

        # Process
        result = self.process(encrypted)

        # Log outcome
        self.audit_logger.log_outcome(result)

        return result

SOC 2 Compliance

For security controls:

class SOC2Controls:
    def __init__(self):
        self.controls = {
            'access_control': self.enforce_access_control,
            'encryption': self.enforce_encryption,
            'monitoring': self.enable_monitoring,
            'change_management': self.track_changes
        }

    def audit_controls(self):
        report = {}
        for control_name, control_func in self.controls.items():
            report[control_name] = control_func()
        return report

Security Auditing

Logging Security Events

Log security-relevant events:

import logging
from datetime import datetime

security_logger = logging.getLogger('security')

def log_security_event(event_type, details):
    security_logger.info({
        'timestamp': datetime.now().isoformat(),
        'event_type': event_type,
        'details': details,
        'source': 'cognitive-engine'
    })

# Usage
log_security_event('AUTHENTICATION_SUCCESS', {
    'user_id': 'user123',
    'ip_address': '192.168.1.1'
})

Intrusion Detection

Implement basic intrusion detection:

class IntrusionDetector:
    def __init__(self):
        self.failed_attempts = {}
        self.blocked_ips = set()

    def check_failed_login(self, ip_address):
        if ip_address in self.failed_attempts:
            self.failed_attempts[ip_address] += 1
        else:
            self.failed_attempts[ip_address] = 1

        if self.failed_attempts[ip_address] >= 5:
            self.blocked_ips.add(ip_address)
            log_security_event('IP_BLOCKED', {'ip': ip_address})

    def is_ip_blocked(self, ip_address):
        return ip_address in self.blocked_ips

Security Monitoring

Monitor security metrics:

from prometheus_client import Counter, Histogram

security_metrics = {
    'failed_auth_attempts': Counter('failed_auth_attempts_total'),
    'api_key_errors': Counter('api_key_errors_total'),
    'suspicious_queries': Counter('suspicious_queries_total'),
    'response_time': Histogram('response_time_seconds')
}

def record_security_metric(metric_name, value=1):
    if metric_name in security_metrics:
        security_metrics[metric_name].inc(value)

Regular Security Audits

Schedule regular security audits:

import schedule
import time

def security_audit():
    """Run security audit checks."""
    print("Running security audit...")

    # Check for exposed API keys
    check_for_exposed_keys()

    # Review access logs
    review_access_logs()

    # Check dependency vulnerabilities
    check_dependencies()

    # Review user permissions
    review_permissions()

    print("Security audit complete")

# Schedule weekly audit
schedule.every().week.do(security_audit)

while True:
    schedule.run_pending()
    time.sleep(60)

Security Best Practices Summary

Do's

✅ Use environment variables for secrets
✅ Encrypt data at rest and in transit
✅ Implement authentication and authorization
✅ Validate all inputs
✅ Keep dependencies updated
✅ Use least privilege access
✅ Log security events
✅ Regular security audits
✅ Use secure coding practices
✅ Implement rate limiting

Don'ts

❌ Commit secrets to version control
❌ Use default credentials
❌ Disable security features
❌ Ignore security warnings
❌ Use outdated dependencies
❌ Expose unnecessary ports
❌ Log sensitive information
❌ Skip input validation
❌ Run as root/admin
❌ Disable encryption

Incident Response

Security Incident Procedure

Detect
Monitor security logs
Set up alerts
Regular security scans
Contain
Isolate affected systems
Block malicious IPs
Revoke compromised credentials
Eradicate
Remove malware/vulnerabilities
Patch security holes
Clean compromised data
Recover
Restore from clean backups
Verify system integrity
Monitor for recurrence
Learn
Document incident
Update procedures
Train team

Incident Reporting

Report security incidents:

def report_incident(incident_details):
    """Report security incident."""
    import smtplib
    from email.mime.text import MIMEText

    msg = MIMEText(f"Security Incident:\n\n{incident_details}")
    msg['Subject'] = 'Security Incident Alert'
    msg['From'] = 'security@cognitiveengine.org'
    msg['To'] = 'security-team@cognitiveengine.org'

    # Send to security team
    # Implementation depends on your email system

Support

For security issues:

Email: autobotsolution@gmail.com
Address: Flushing MI
Report security vulnerabilities responsibly
Do not disclose publicly before fix