README.md

ai-security-alerts

Security monitoring system that logs suspicious activities and alerts your security team, allowing you to make informed decisions about escalating genuine threats.

Written by: DeWitt Gibson https://linkedin.com/in/dewitt-gibson/

AI Model Security Monitor

Real-time security monitoring and team alert system for AI model deployments. Detects threats, alerts security teams, and logs suspicious activities.

Features

• Real-time threat detection
• Automatic security team notifications
• Rate limiting and pattern analysis
• Incident logging and reporting
• IP-based monitoring
• Request pattern analysis
• Configurable alert thresholds
• SMTP email notifications

Installation

pip install -r requirements.txt

Required dependencies in requirements.txt:

numpy>=1.21.0
typing>=3.7.4
smtplib
email
datetime
logging

Usage

from ai_security_monitor import AISecurityMonitor

# Initialize monitor
monitor = AISecurityMonitor(
    model_name="production_model",
    alert_settings={
        "email_recipients": ["[email protected]"],
        "smtp_settings": {
            "server": "smtp.company.com",
            "port": 587,
            "sender": "[email protected]",
            "use_tls": True,
            "username": "alert_system",
            "password": "your_secure_password"
        },
        "alert_thresholds": {
            "max_requests_per_minute": 100,
            "suspicious_pattern_threshold": 0.8,
            "failed_attempts_threshold": 5
        }
    }
)

# Monitor requests
threat_assessment = monitor.detect_threat({
    "ip_address": request.remote_addr,
    "input_data": model_input,
    "timestamp": datetime.now()
})

# Get incident summary
summary = monitor.get_incident_summary(hours=24)

Configuration

Environment Variables

SMTP_SERVER=smtp.company.com
SMTP_PORT=587
[email protected]
[email protected]

Alert Thresholds

{
    "max_requests_per_minute": 100,  # Maximum requests per minute per IP
    "suspicious_pattern_threshold": 0.8,  # Threshold for pattern detection
    "failed_attempts_threshold": 5  # Maximum failed attempts before alert
}

Threat Detection

The monitor detects:

• Rate limit violations
• Suspicious input patterns
• Repeated failed attempts
• Unusual request patterns
• Potential adversarial attacks

Logging

Logs are saved to: security_{model_name}_{date}.log

Log format:

timestamp - level - message

Security Considerations

• Secure SMTP credentials
• Monitor alert thresholds
• Regular log review
• Update recipient list
• Rotate credentials

Contributing

See CONTRIBUTING.md

License

MIT License - See LICENSE