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ai-model-security-monitor

Security monitoring tool that helps protect AI models from common attacks.

Author: DeWitt Gibson https://www.linkedin.com/in/dewitt-gibson/

The tool provides these key security features:

Input validation:

Size limits Value range checks Null/empty input detection Format validation

Attack detection:

Adversarial pattern detection Unusual input structure detection Gradient analysis

Request monitoring:

Request logging Rate monitoring Repeated input detection Pattern analysis

Detailed reporting:

Validation results Detected issues Request analysis Security recommendations

Security Team Alerting

AI Model Security Tools

A comprehensive security toolkit for protecting AI model deployments, including model protection, threat monitoring, and security assessment capabilities.

Components

  1. AIModelProtector: Real-time protection and monitoring for AI model endpoints
  2. AISecurityMonitor: Advanced threat detection and team notification system
  3. ChatbotThreatModeler: Threat assessment and security evaluation for chatbot implementations

Installation

pip install -r requirements.txt

Required dependencies:

numpy>=1.21.0
pandas>=1.3.0
typing>=3.7.4
logging>=0.5.1.2
smtplib
email

Quick Start

Basic Protection Setup

from ai_model_protector import AIModelProtector
from ai_security_monitor import AISecurityMonitor
from chatbot_threat_modeler import ChatbotThreatModeler

# Initialize base protection
protector = AIModelProtector(
    model_name="production_model",
    input_constraints={
        "max_size": 1000000,
        "max_value": 1.0,
        "min_value": -1.0,
        "max_gradient": 50
    }
)

# Setup security monitoring
monitor = AISecurityMonitor(
    model_name="production_model",
    alert_settings={
        "email_recipients": ["security@company.com"],
        "smtp_settings": {
            "server": "smtp.company.com",
            "port": 587,
            "sender": "ai-alerts@company.com",
            "use_tls": True
        },
        "alert_thresholds": {
            "max_requests_per_minute": 100,
            "suspicious_pattern_threshold": 0.8
        }
    }
)

# Initialize threat modeling
threat_modeler = ChatbotThreatModeler()

Deployment Integration

def process_model_request(input_data, request_metadata):
    # 1. Check security protections
    security_check = protector.protect(input_data)
    if not security_check["allow_inference"]:
        return {"error": "Security check failed", "details": security_check}
    
    # 2. Monitor for threats
    threat_assessment = monitor.detect_threat({
        "ip_address": request_metadata["ip"],
        "input_data": input_data
    })
    
    if threat_assessment["severity"] == "high":
        return {"error": "Request blocked due to security risk"}
    
    # 3. Process request if safe
    try:
        prediction = model.predict(input_data)
        return {"prediction": prediction}
    except Exception as e:
        monitor.log_incident({
            "type": "inference_error",
            "details": str(e)
        })
        return {"error": "Processing failed"}

Security Features

Model Protection

  • Input validation and sanitization
  • Pattern analysis for adversarial attacks
  • Request rate limiting
  • Anomaly detection

Security Monitoring

  • Real-time threat detection
  • Team notifications
  • Incident logging
  • Traffic analysis
  • IP-based monitoring

Threat Modeling

  • Security control assessment
  • Risk scoring
  • Threat identification
  • Compliance checking
  • Recommendation generation

Configuration

Environment Variables

SECURITY_LOG_PATH=/path/to/logs
ALERT_SMTP_SERVER=smtp.company.com
ALERT_SMTP_PORT=587
ALERT_SENDER=ai-security@company.com
ALERT_RECIPIENTS=security-team@company.com

Security Thresholds

SECURITY_CONFIG = {
    "max_requests_per_minute": 100,
    "suspicious_pattern_threshold": 0.8,
    "max_failed_attempts": 5,
    "session_timeout": 3600,
    "min_request_interval": 1.0
}

Best Practices

  1. API Key Management

    • Rotate keys regularly
    • Use separate keys for different environments
    • Monitor key usage

  2. Logging

    • Enable comprehensive logging
    • Store logs securely
    • Implement log rotation

  3. Monitoring

    • Set up alerts for suspicious activities
    • Monitor resource usage
    • Track error rates

  4. Regular Assessment

    • Run threat modeling weekly
    • Review security controls
    • Update security thresholds

Contributing

Please see CONTRIBUTING.md for guidelines on contributing to this project.

License

MIT License - See LICENSE.md for details