The problems this exists to solve.
Every model and agent, listed where it runs.
- GitHubConnected1 asset found
- DatabricksConnected1 asset found
- AWS SageMakerConnected2 assets found
- Google Vertex AIConnected1 asset found
- Hugging FaceConnected1 asset found
- MLflowNot connectedConnect in console
- Apache AirflowConnected1 asset found
- KubernetesConnected1 asset found
- Azure Machine LearningNot connectedConnect in console
- Weights & BiasesNot connectedConnect in console
Built for enterprise AI.
How it works for your team, day to day.
What Runtime Discovery finds
Runtime Discovery connects to the cloud and ML platforms where your teams build AI and lists what it finds: a fraud-scoring model in AWS SageMaker, a RAG pipeline in Databricks, a claims agent on Google Vertex AI, an Apache Airflow workflow, a fine-tuned model on Hugging Face, an inference deployment on Kubernetes.
One AI asset inventory, from platform to laptop
Models and agents in your ML platforms are half the picture. The endpoint view adds the AI on employee devices: the AI assets graph for each device, an AI inventory for each employee, and every MCP server with a 0 to 100 risk score, capability tags and findings. Together they give you a component list built from what is running, the starting point for an AI-BOM.
From discovery to runtime security
For the LLM apps and agents your teams build, the Agent Gateway SDK puts checks in the path of every action. Identity, authorization, the action and its risk are checked inline, documents, RAG retrievals and tool outputs are scanned for prompt injection, and the action is allowed, redacted, held for approval or blocked. Violations alert immediately by email, Slack or Microsoft Teams, and audit logs stream to Splunk, Microsoft Sentinel, Google SecOps, IBM QRadar or Elastic.
The questions we hear most.
What is AI model discovery?
AI model discovery is finding the AI models, agents and pipelines an organization runs, across the platforms where they are built and deployed. AccuroAI Runtime Discovery connects to platforms such as AWS SageMaker, Databricks and Google Vertex AI and lists the models, agents, APIs, pipelines and workflows it finds in one inventory.
Can AccuroAI discover AI models in SageMaker or Databricks?
Yes. Runtime Discovery finds AI models, agents, APIs, pipelines and workflows across GitHub, Databricks, AWS SageMaker, Google Vertex AI, Hugging Face, MLflow, Apache Airflow, Kubernetes, Azure Machine Learning and Weights & Biases. Each platform is connected from the AccuroAI console.
Can AccuroAI help build an AI bill of materials (AI-BOM)?
It gives you the inventory an AI-BOM starts from. Runtime Discovery lists the models, agents, APIs, pipelines and workflows in your ML platforms, and the endpoint view adds the AI apps, agents and MCP servers on employee devices, so the component list comes from what is running instead of a survey.
Can AccuroAI secure the internal LLM apps it finds?
Yes, through the Agent Gateway SDK. Your apps and agents call Agent Gateway before each action. It checks identity, authorization, the action and its risk inline, scans documents, RAG retrievals and tool outputs for prompt injection, and returns a verdict: Allowed, Redacted, Held for approval or Blocked.
What if our ML platform is not on the list?
Runtime Discovery connects today to GitHub, Databricks, AWS SageMaker, Google Vertex AI, Hugging Face, MLflow, Apache Airflow, Kubernetes, Azure Machine Learning and Weights & Biases. Don't see your tool? New integrations are usually a few hours of work, so bring your stack to the pilot.