Protect model deployment environment

Implementing model access protection. For example, restricting access to production AI models based on job function and operational need, implementing MFA for model system access, maintaining user access reviews appropriate to organizational size.

Establishing deployment security controls. For example, applying scoped API tokens or signed requests, enforcing rate limits, using TLS for all endpoint traffic, implementing schema validation to protect model APIs from malformed or adversarial input.

Securing model hosting environments. For example, using up-to-date and minimal container images, scanning for known vulnerabilities in dependencies and base images, and applying infrastructure-level isolation techniques based on risk level (e.g. container namespaces, VM separation, or dedicated GPU access).

Verifying model integrity before and during deployment. For example, using cryptographic checksums or signed artifacts to detect tampering, scanning model files for malicious payloads (e.g. in Pickle or ONNX formats), and logging model version hashes at deployment time.