Merge pull request #1 from dewitt4/add

Initial PR

CONTRIBUTING.md

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+# Contributing to AI Model Security Tools
+
+Thank you for your interest in contributing to this security toolkit. We welcome contributions that improve the security, reliability, and usability of these tools.
+
+## Core Principles
+
+1. **Do No Harm**: All contributions must be intended for defensive security purposes only
+2. **Privacy First**: Never collect or expose sensitive user data
+3. **Transparency**: Document all security mechanisms and changes
+4. **Responsibility**: Test thoroughly before submitting changes
+
+## How to Contribute
+
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-security-feature`)
+3. Commit your changes (`git commit -m 'Add new security feature'`)
+4. Push to your branch (`git push origin feature/amazing-security-feature`)
+5. Open a Pull Request
+
+## Development Guidelines
+
+- Add tests for any new features
+- Update documentation for changes
+- Follow PEP 8 style guidelines
+- Use type hints for all new code
+- Add logging for security-relevant events
+- Comment security-critical code sections
+
+## Security Requirements
+
+- No code that could enable attacks or exploitation
+- No weakening of existing security measures
+- No collection of unnecessary user data
+- No hard-coded credentials or secrets
+- No disabled security features by default
+
+## Testing
+
+- Add unit tests for new features
+- Include integration tests where appropriate
+- Test edge cases and error conditions
+- Verify no security weaknesses introduced
+
+## Documentation
+
+When adding or modifying features:
+- Update README.md
+- Add docstrings to functions/classes
+- Document security implications
+- Include usage examples
+
+## Need Help?
+
+- Open an issue for bugs or security concerns
+- Use discussions for feature ideas
+- Tag security-critical issues appropriately
+
+## Code of Conduct
+
+- Be respectful and constructive
+- Focus on improving security
+- No malicious or harmful contributions
+- Report security issues responsibly
+
+## License
+
+By contributing, you agree that your contributions will be licensed under the MIT License.

README.md

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 # ai-model-security-monitor
 Security monitoring tool that helps protect AI models from common attacks. 
+
+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
+
+```bash
+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
+
+```python
+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": ["[email protected]"],
+        "smtp_settings": {
+            "server": "smtp.company.com",
+            "port": 587,
+            "sender": "[email protected]",
+            "use_tls": True
+        },
+        "alert_thresholds": {
+            "max_requests_per_minute": 100,
+            "suspicious_pattern_threshold": 0.8
+        }
+    }
+)
+
+# Initialize threat modeling
+threat_modeler = ChatbotThreatModeler()
+```
+
+### Deployment Integration
+
+```python
+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
+```bash
+SECURITY_LOG_PATH=/path/to/logs
+ALERT_SMTP_SERVER=smtp.company.com
+ALERT_SMTP_PORT=587
+[email protected]
+[email protected]
+```
+
+### Security Thresholds
+```python
+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