🤖 Lesson 1: Introduction to AI Security
Understanding AI systems and their unique security challenges
📚 Learning Objectives
By the end of this lesson, you will be able to:
- Define AI and machine learning security concepts
- Identify unique security challenges in AI systems
- Understand the AI attack surface
- Recognize security vs. performance trade-offs
- Map the AI security lifecycle
🤖 What is AI Security?
Definition
AI Security is the practice of protecting artificial intelligence systems, machine learning models, and their associated data from malicious attacks, unauthorized access, and other security threats.
🔑 Key Components of AI Security:
- Model Security: Protecting ML models from attacks
- Data Security: Securing training and inference data
- Infrastructure Security: Securing AI deployment environments
- Pipeline Security: Securing the ML development lifecycle
Why AI Security is Critical
AI systems are increasingly being deployed in critical applications including:
- Autonomous vehicles and transportation
- Healthcare diagnostics and treatment
- Financial services and fraud detection
- Cybersecurity and threat detection
- National security and defense
🏗️ AI System Components
Training Data
Purpose: Dataset used to train machine learning models
🔒 Security Considerations:
- Data poisoning attacks
- Privacy leakage
- Data integrity
- Access controls
Model Architecture
Purpose: The structure and design of the ML model
🔒 Security Considerations:
- Model extraction
- Architecture vulnerabilities
- Backdoor insertion
- Model inversion
Inference Pipeline
Purpose: System that processes new data using trained models
🔒 Security Considerations:
- Adversarial examples
- Input validation
- API security
- Resource exhaustion
Deployment Infrastructure
Purpose: Hardware and software environment running AI systems
🔒 Security Considerations:
- Infrastructure attacks
- Supply chain risks
- Runtime security
- Network security
⚠️ Unique Security Challenges in AI
1. Adversarial Attacks
Malicious inputs designed to fool AI models into making incorrect predictions.
Example:
Adding imperceptible noise to an image to make a classifier misidentify a stop sign as a speed limit sign.
2. Data Poisoning
Corrupting training data to influence model behavior during inference.
Example:
Injecting malicious samples into training data to create backdoors in the model.
3. Model Extraction
Stealing AI models through querying and reverse engineering.
Example:
Querying a model API thousands of times to reconstruct the model's parameters.
4. Privacy Attacks
Extracting sensitive information from models or training data.
Example:
Using model inversion attacks to reconstruct training images from a facial recognition model.
🎯 AI Attack Surface
AI System Attack Surface
Data Layer
- Training data poisoning
- Data leakage
- Privacy attacks
Model Layer
- Adversarial examples
- Model extraction
- Backdoor attacks
Infrastructure Layer
- System compromise
- Supply chain attacks
- Runtime attacks
⚖️ Security vs. Performance Trade-offs
Common Trade-offs in AI Security
Privacy vs. Utility
Adding privacy protections (like differential privacy) can reduce model accuracy.
Robustness vs. Accuracy
Making models more robust to adversarial attacks may decrease performance on clean data.
Security vs. Speed
Implementing security measures can increase inference time and computational overhead.
Transparency vs. Security
More transparent models may be easier to attack but easier to audit.
🔄 AI Security Lifecycle
Design Phase
Define security requirements, threat model, and security architecture.
Data Collection
Secure data collection, validation, and preprocessing with privacy protections.
Model Training
Implement secure training practices and validation.
Testing & Validation
Security testing, adversarial testing, and robustness validation.
Deployment
Secure deployment with monitoring and access controls.
Monitoring & Maintenance
Continuous monitoring, incident response, and model updates.
🧪 Hands-On Exercise
Exercise: AI Security Threat Assessment
Objective: Analyze a hypothetical AI system and identify potential security threats.
📋 Steps:
-
System Analysis
Consider an AI-powered fraud detection system for online banking:
- Uses customer transaction data for training
- Deployed via API for real-time fraud detection
- Updates model monthly with new data
-
Threat Identification
Identify potential threats for each component:
- Training data: What could go wrong?
- Model: What attacks are possible?
- API: What security issues exist?
- Infrastructure: What vulnerabilities are there?
-
Risk Assessment
Prioritize threats based on:
- Likelihood of occurrence
- Potential impact
- Difficulty to detect
📄 Deliverables:
- Threat landscape diagram
- Risk assessment matrix
- Recommended security controls
- Incident response plan outline