AI Incident Response - AI Security Module 4 Lesson 1

🎯 Learning Objectives

Design AI-specific incident response procedures
Implement threat detection and classification systems
Execute containment and eradication strategies
Develop recovery and lessons learned processes
Create communication protocols for AI incidents

📚 Core Concepts

1. AI Incident Classification

Understanding different types of AI security incidents and their severity levels.

Incident Types

Adversarial Attacks: Targeted manipulation of AI models
Data Poisoning: Malicious training data injection
Model Theft: Unauthorized access to AI models
Privacy Breaches: Unauthorized data access
Bias Exploitation: Manipulation of model biases

Severity Levels

Critical

Complete system compromise, data breach, or safety risk

High

Significant performance degradation or security threat

Medium

Moderate impact on operations or security

Low

Minor issues with minimal impact

2. Incident Response Lifecycle

Six-phase approach to handling AI security incidents effectively.

1. Preparation

Build capabilities, train teams, create procedures

2. Identification

Detect and confirm security incidents

3. Containment

Isolate affected systems and prevent spread

4. Eradication

Remove threats and vulnerabilities

5. Recovery

Restore systems and normal operations

6. Lessons Learned

Analyze incident and improve processes

🔧 Implementation Strategies

1. Incident Detection Framework

Comprehensive monitoring and alerting system for AI security incidents.

class AIIncidentDetector:
    def __init__(self, monitoring_config):
        self.monitoring_config = monitoring_config
        self.detectors = {
            'adversarial': AdversarialDetector(),
            'data_drift': DataDriftDetector(),
            'performance': PerformanceDetector(),
            'privacy': PrivacyViolationDetector()
        }
        
    def detect_incident(self, model_data, input_data, predictions):
        """Detect potential AI security incidents"""
        incidents = []
        
        for detector_type, detector in self.detectors.items():
            result = detector.analyze(model_data, input_data, predictions)
            if result['is_incident']:
                incidents.append({
                    'type': detector_type,
                    'severity': result['severity'],
                    'confidence': result['confidence'],
                    'details': result['details'],
                    'timestamp': time.time()
                })
        
        return incidents
    
    def classify_incident(self, incident):
        """Classify incident severity and type"""
        severity_mapping = {
            'critical': ['model_compromise', 'data_breach', 'safety_violation'],
            'high': ['adversarial_attack', 'performance_degradation'],
            'medium': ['data_drift', 'privacy_concern'],
            'low': ['anomaly_detected', 'minor_performance_issue']
        }
        
        for severity, types in severity_mapping.items():
            if incident['type'] in types:
                return severity
        
        return 'unknown'

2. Automated Response System

Automated containment and initial response capabilities.

class AutomatedResponseSystem:
    def __init__(self, response_policies):
        self.policies = response_policies
        self.actions = {
            'isolate_model': self.isolate_model,
            'block_inputs': self.block_inputs,
            'switch_fallback': self.switch_fallback,
            'alert_team': self.alert_team
        }
        
    def execute_response(self, incident):
        """Execute automated response based on incident type"""
        policy = self.policies.get(incident['type'], {})
        actions = policy.get('actions', [])
        
        executed_actions = []
        for action in actions:
            if action in self.actions:
                result = self.actions[action](incident)
                executed_actions.append({
                    'action': action,
                    'result': result,
                    'timestamp': time.time()
                })
        
        return executed_actions
    
    def isolate_model(self, incident):
        """Isolate compromised model from production traffic"""
        # Implementation details
        return {'status': 'isolated', 'model_id': incident['model_id']}
    
    def switch_fallback(self, incident):
        """Switch to fallback model"""
        # Implementation details
        return {'status': 'switched', 'fallback_model': 'model_v2'}

🚨 AI Incident Response