What is AI Model Security?

What it does

AI Model Security tools protect the machine learning models that organizations build and deploy. They work across the model lifecycle and typically perform some combination of the following:

• Scanning model files and artifacts for malicious code or embedded vulnerabilities before deployment
• Assessing models for known weaknesses, such as susceptibility to adversarial inputs or extraction attacks
• Encrypting model weights and inference pipelines so that training data and proprietary algorithms are not exposed
• Enabling model training and inference on sensitive data without exposing that data to other parties, using techniques such as confidential computing or privacy-enhancing technologies (PETs)
• Scoring models against security, safety, and reliability benchmarks so teams can compare risk across model versions

The category sits inside the broader AI Security parent category, alongside related disciplines such as AI Data Poisoning Protection, AI Red Teaming, and MLSecOps.

Why teams buy it

Organizations invest in AI Model Security for three main reasons.

First, trained models represent significant intellectual property. A stolen model can be copied, fine-tuned, or used to reconstruct proprietary training data.

Second, deployed models are attack surfaces. Adversaries can craft inputs that cause misclassification, extract training data through repeated queries, or inject malicious behavior into model artifacts.

Third, regulated industries face compliance pressure. Healthcare, finance, and defense organizations must demonstrate that models handling sensitive data are protected at rest and in use.

What to look for

When evaluating tools in this category, consider:

• Coverage across the lifecycle: Does the tool address pre-deployment scanning, runtime inference protection, and training-time privacy?