AI Security Infrastructure: Threat Modeling for Machine Learning Systems

Traditional application security covers input validation, authentication, and network security. AI systems have additional attack surfaces that traditional security teams don't know to look for: adversarial examples, model extraction, training data poisoning, and prompt injection.

AI-Specific Threat Landscape

• Adversarial inputs — Crafted inputs that cause misclassification while appearing normal to humans
• Model extraction — Systematic querying to reconstruct your proprietary model
• Data poisoning — Corrupting training data to introduce backdoors or bias
• Model inversion — Extracting training data from model responses
• Prompt injection — For LLM-based systems, manipulating system behavior through crafted prompts
• Supply chain attacks — Compromised pre-trained models, poisoned datasets, or malicious dependencies

Infrastructure Defenses

Input Validation Layer

Deploy input validation before the model: statistical anomaly detection on input features, adversarial example detection (feature squeezing, input transformation), rate limiting per client to prevent model extraction, and input logging for forensic analysis.

Model Serving Hardening

Limit information leakage from model responses: return class labels instead of probability distributions where possible, add calibrated noise to confidence scores, implement query budgets per API consumer, monitor for systematic probing patterns.

Training Pipeline Security

Protect the training pipeline: cryptographic verification of training data sources, anomaly detection on training metrics (sudden accuracy changes may indicate poisoning), signed model artifacts with provenance tracking, isolated training environments with no internet access.

Red Teaming for AI Systems

Regular adversarial testing of your AI systems — not just penetration testing of the infrastructure, but actual attacks against the models. This should be part of your continuous security program, not a one-time assessment.