🛡️ Lesson 4: Basic AI Security Controls
Implementing fundamental security measures for AI systems
📚 Learning Objectives
By the end of this lesson, you will be able to:
- Implement secure data handling practices
- Apply model validation and testing techniques
- Configure access controls for AI systems
- Set up monitoring and logging
- Develop incident response procedures for AI
🔐 Secure Data Handling Practices
Data Security Controls
1. Data Classification and Labeling
Implementation Steps:
- Classify data based on sensitivity levels
- Label datasets with security markings
- Implement data handling procedures
- Create data retention policies
Security Benefits:
- Clear data handling requirements
- Appropriate access controls
- Compliance with regulations
- Risk-based protection measures
2. Data Encryption
Encryption Requirements:
- Data at Rest: Encrypt stored datasets
- Data in Transit: Secure data transmission
- Data in Processing: Homomorphic encryption for computation
- Key Management: Secure key storage and rotation
3. Access Controls
Access Management:
- Role-based access control (RBAC)
- Multi-factor authentication
- Least privilege principle
- Regular access reviews
🧪 Model Validation and Testing
Security Testing Framework
1. Adversarial Testing
Testing Approaches:
- FGSM Testing: Fast gradient sign method attacks
- PGD Testing: Projected gradient descent attacks
- C&W Testing: Carlini & Wagner attacks
- Universal Attacks: Universal adversarial perturbations
Implementation:
# Example adversarial testing with CleverHans
import tensorflow as tf
from cleverhans.tf2.attacks import fast_gradient_method
def test_adversarial_robustness(model, x_test, y_test):
# Generate adversarial examples
adv_x = fast_gradient_method(model, x_test, eps=0.3, norm=np.inf)
# Test model performance
clean_acc = model.evaluate(x_test, y_test)[1]
adv_acc = model.evaluate(adv_x, y_test)[1]
print(f"Clean accuracy: {clean_acc:.3f}")
print(f"Adversarial accuracy: {adv_acc:.3f}")
return clean_acc, adv_acc
2. Data Poisoning Detection
Detection Methods:
- Statistical Analysis: Detect anomalies in training data
- Cross-Validation: Identify suspicious data points
- Outlier Detection: Find poisoned samples
- Data Lineage Tracking: Monitor data sources
3. Privacy Testing
Privacy Assessment:
- Membership Inference: Test for data leakage
- Model Inversion: Attempt data reconstruction
- Attribute Inference: Test for attribute leakage
- Differential Privacy: Verify privacy guarantees
🔒 Access Control for AI Systems
AI-Specific Access Controls
1. Model Access Controls
Access Restrictions:
- Read Access: View model predictions only
- Query Access: Submit inference requests
- Admin Access: Manage model configurations
- Developer Access: Model development and training
2. Data Access Controls
Data Protection:
- Training Data: Restricted access to sensitive datasets
- Inference Data: Secure handling of input data
- Model Parameters: Protect intellectual property
- Logs and Metrics: Secure audit trails
3. API Access Controls
API Security:
- Authentication: API keys and tokens
- Authorization: Role-based permissions
- Rate Limiting: Prevent abuse and DoS
- Input Validation: Sanitize API inputs
📊 Monitoring and Logging
AI Security Monitoring
1. Model Performance Monitoring
Key Metrics:
- Accuracy Drift: Monitor performance degradation
- Prediction Confidence: Track uncertainty levels
- Input Distribution: Detect data drift
- Response Times: Monitor system performance
2. Security Event Monitoring
Security Indicators:
- Failed Authentication: Track login attempts
- Unusual API Usage: Detect potential attacks
- Data Access Patterns: Monitor data usage
- Model Modifications: Track changes
3. Compliance Monitoring
Compliance Tracking:
- Data Privacy: GDPR, CCPA compliance
- Audit Trails: Complete activity logs
- Access Reviews: Regular access audits
- Incident Reporting: Security event documentation
🚨 Incident Response for AI
AI Security Incident Response
1. Detection and Analysis
Detection Methods:
- Automated Monitoring: Real-time threat detection
- Anomaly Detection: Unusual behavior identification
- User Reports: Human-identified issues
- External Intelligence: Threat intelligence feeds
Analysis Steps:
- Confirm security incident
- Assess impact and scope
- Identify attack vectors
- Determine affected systems
2. Containment and Eradication
Containment Actions:
- Isolate Systems: Quarantine affected components
- Disable Access: Block malicious users
- Preserve Evidence: Collect forensic data
- Notify Stakeholders: Internal and external communication
Eradication Steps:
- Remove malicious code
- Patch vulnerabilities
- Update security controls
- Validate system integrity
3. Recovery and Lessons Learned
Recovery Process:
- System Restoration: Restore normal operations
- Data Recovery: Restore from backups
- Security Updates: Implement improvements
- Monitoring Enhancement: Strengthen detection
Post-Incident Activities:
- Conduct post-mortem analysis
- Update incident response plan
- Train staff on lessons learned
- Improve security measures
🧪 Hands-On Exercise
Exercise: Implement Basic AI Security Controls
Objective: Set up fundamental security controls for an AI system.
📋 Implementation Tasks:
Task 1: Data Security Setup
- Implement data classification system
- Set up encryption for data at rest
- Configure secure data transmission
- Establish access controls for datasets
Task 2: Model Security Testing
- Implement adversarial testing framework
- Set up automated security testing
- Configure model validation pipeline
- Create security testing reports
Task 3: Access Control Implementation
- Configure role-based access control
- Set up API authentication
- Implement rate limiting
- Configure input validation
Task 4: Monitoring and Logging
- Set up security event monitoring
- Configure audit logging
- Implement alert systems
- Create monitoring dashboards
📄 Deliverables:
- Security control implementation documentation
- Testing results and validation reports
- Access control configuration
- Monitoring and alerting setup
- Incident response procedures