The Model Security identity enhancement establishes a centralized model registry for the AI models you scan, moving away from fragile, location-based tracking that relies on URI file paths. Instead, you can now utilize artifact fingerprinting to establish a definitive, content-based identity for every model. By providing a structured data model with robust artifact management and customizable display names, this feature allows you to register models securely and maintain a persistent identity that reflects your business needs rather than your storage topology.

You can apply this framework to seamlessly track the evolution of your AI models as they change over time. Through automated version tracking, every new scan or update of an existing model automatically increments its version number, ensuring you retain a complete historical record of model changes and their associated artifacts. Whether you are an MLOps engineer migrating models across environments or a security professional auditing scan results, you can trace the exact model version and its specific component artifacts. Because the system uses content-based fingerprinting rather than temporary storage locations, your model's identity remains completely intact even if you move artifacts from an S3 bucket to a local deployment path.

Adopting this robust identity approach ensures you maintain continuous visibility and strict audit trails for your scanned AI assets. You gain the ability to assign business-relevant display names to your models, replacing obscure file paths with recognizable identities throughout your UI and API responses. Ultimately, this enhanced registration and versioning capability eliminates the loss of audit trails when models move between environments, providing you with automated lifecycle visibility and a highly reliable security posture for your AI deployments.