Securing AI E
Comprehensive strategies to identify, mitigate, and monitor threats across machine learning systems and data infrastructure
Key Concepts
Data Poisoning
Attack vector where input data is manipulated to degrade model performance
Model Inversion
Reconstructing sensitive training data from model outputs
Adversarial Attacks
Input perturbations that fool machine learning models
Threat Modeling Framework
STRIDE Methodology
System Threat and Risk Analysis for AI Systems
Scope
Define assets and attack surfaces
Threats
Identify potential risks
Response
Design mitigation strategies
Defense Strategies
Input Validation
Sanitize all incoming data with domain-specific filters
tf.data.Dataset.map(cleansing_function)
Model Robustness
Train with adversarial examples and differential privacy
adv_trainer.train_with_foolbox()
Recommended Tools
Foolbox
Adversarial attack & robustness testing library
DVC
Data version control for audit trails
MMD
Machine learning model monitoring