Products

Model Identity Monitor

Confirm model identities at runtime

Our behavioral fingerprinting technology creates unique, verifiable fingerprints for AI models based on their runtime behavior. This enables precise, continuous model identification and verification across different deployment environments, providing unprecedented visibility into AI model operations.

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Identify

Runtime fingerprinting allows for applications to identify which models are being accessed for model inference. Ensure the model wasn't switched or modified significantly from pre to post deployments.

Compare

Fingerprints are comparable so you can determine if two models are similar to each other. This unlocks efficient checking to see if a model will likely behave similarly to another model.

Detect

Open source models make it difficult to track model provenance. With fingerprinting & similarity measuring, you can detect if prohibited models are being accessed within your secure environment.

Trust

How you do grant access to models and agentic systems? Fingerprinting enables an organization to decide which models are trusted with access to data, compute resources, and tool use.

Model Fingerprint Registry

Track & trace models your applications depend on

The Model Fingerprint Registry is purpose-built to allow an application to track & trace the models it depends on. Unlike traditional model registries, the Model Fingerprint Registry is not a single, centralized registry but a lightweight registry that is easy to spin up for new applications to ensure the right models are being used.

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Discover

Use the registry to benchmark different models across an entire application or for different tasks within an application.

Observe

Capture performance and other individual model observations within the registry. Evaluations about model behavior within an application can be shared with others.

Integrate

Integrate with CI/CD pipelines to ensure models have not changed prior to deployment. Trigger notifications to application owners about changes to models.

Manage

Enable different departments to maintain different registries across large organizations. Applications can be given access to specific models through a managed registry service.

Verifiable Model Endpoints

Securely deploy custom models with runtime verification.

Verifiable Endpoints allow you to deploy custom models within protected environments and provide downstream services the ability to audit or verify runtime behavior. This provides assurance that the correct models were used in sensitive applications.

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Deploy

Ensure the correct model is deployed within a secure network. Use fingerprints to guarantee the model running matches the expected model.

Verify

Verify the correct and appropriate model is running before granting access to data, MCP tools, code execution, and other sensitive resources.

Assure

Maintain network integrity by ensuring only approved models are deployed within the network. Continuously verify model endpoints and get alerts if the model has changed.

Comply

Automate compliance by generating reports about model deployments within the organization. Conform to requirements for each jurisdiction the organization operates in.

Industry Examples

Financial Services

Large banks operate in many jurisdictions around the world. Each jurisdiction has its own requirements for which models can be used for different banking operations: lending decisions, fraud detection, credit scoring, etc. Using the Model Fingerprint Registry, a bank can ensure only approved models are used by applications in each jurisdiction. Each application can retrieve an updated list of models approved for use where the application will reside. Model access can be monitored and audited through the registry and model fingerprints.

Healthcare

Healthcare stands to dramatically improve through the use of frontier AI. New models are emerging for a plethora of healthcare applications: disease specific models, diagnostic tools, clinical decision support, etc. Additionally, these models are continually improved as new information becomes available. Using Behavioral Monitoring, healthcare applications can determine if a new model is similar to approved models and detect if the models have drifted significantly from currently deployed models. The amount of drift can be quantified and used to determine if the model is safe to use.

Government & Defense

Military organizations utilize AI models in the most adversarial environments. It is critical to ensure AI models are evaluated thoroughly before being deployed. It is equally critical to ensure the evaluated models (and only those models) are deployed in the battlefield. Military applications can use verifiable endpoints combined with secure compute hardware to provide cryptographic guarantees that the correct models are being accessed. Moreover, they can guarantee no adversarial models are allowed into the information supply chain.

Enterprise AI Platforms

When an enterprise adopts a coding assistant like GPT-OSS to do code generation, the model's identity (GPT-OSS-120B) should be verified at the start of each session. This provides assurances to the developer that the models responses are suitable for their task. Upon verification, the coding assistant can allow the model to generate code, execute it on the enterprise developer's machine or in the enterprise's cloud environment. Additionally, the model can be given access to sensitive or private data otherwise off limits.