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https://github.com/dewitt4/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.
https://github.com/dewitt4/ai-security-alerts
ai ai-sec ai-security cybersecurity llm-security
Last synced: 16 days ago
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Security monitoring system that logs suspicious activities and alerts your security team, allowing you to make informed decisions about escalating genuine threats.
- Host: GitHub
- URL: https://github.com/dewitt4/ai-security-alerts
- Owner: dewitt4
- License: mit
- Created: 2024-11-22T16:29:00.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-22T16:40:08.000Z (about 2 months ago)
- Last Synced: 2024-11-22T17:31:22.808Z (about 2 months ago)
- Topics: ai, ai-sec, ai-security, cybersecurity, llm-security
- Language: Python
- Homepage:
- Size: 8.79 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# 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
```bash
pip install -r requirements.txt
```
Required dependencies in requirements.txt:
```
numpy>=1.21.0
typing>=3.7.4
smtplib
email
datetime
logging
```
## Usage
```python
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
```bash
SMTP_SERVER=smtp.company.com
SMTP_PORT=587
[email protected]
[email protected]
```
### Alert Thresholds
```python
{
"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](CONTRIBUTING.md)
## License
MIT License - See [LICENSE](LICENSE)