AI Model Security | Model Protection
Security controls for AI and ML model protection — covering model theft prevention, training data poisoning detection, adversarial input defence, and model access controls.
AI Model Security — Protect ML Models from Theft and Abuse
Access controls, adversarial input defences, training data integrity monitoring, and usage auditing to protect AI and ML models from extraction, poisoning, adversarial manipulation, and unauthorised access.
- Model API authentication and per-user rate limiting to prevent extraction attacks
- Extraction attempt detection — statistical analysis of systematic querying patterns
- Training data integrity monitoring for poisoning indicators in fine-tuning pipelines
- Adversarial input detection for security-critical ML classification models
- Full model usage audit trail with anomaly detection on access patterns
- Supply chain risk assessment for third-party models and fine-tuning datasets
Model Extraction Is IP Theft
A trained model represents months of data collection and compute investment. Systematic API querying can clone model behaviour without accessing weights or training data — effectively stealing the model.
Poisoned Models Fail at Scale
A successfully poisoned model causes systematic incorrect decisions across every user and use case — impact scales with model adoption, making training data integrity critical.
Adversarial Robustness for Security-Critical ML
Fraud detection, malware classification, and access control models are high-value adversarial targets — they require specific defences against inputs crafted to cause misclassification.