ml-pipeline-security-review

What problem does it solve?

This Skill addresses the security vulnerabilities in your machine learning pipeline, ensuring data integrity, access control, and model provenance.

Core Features & Use Cases

• Data Poisoning Surface Analysis: Identify potential data poisoning risks and ensure data trustworthiness.
• Feature Store Trust and Access Control: Ensure the integrity and access control of feature stores and data lakes.
• Experiment Tracking and Model Registry Security: Secure access to MLflow, W&B, or other registries with proper authentication and authorization.
• Secrets and Infrastructure Security: Protect credentials and infrastructure from unauthorized access.
• Reproducibility and Provenance: Ensure that models can be traced back to their data and code.
• Use Case: Use this Skill to assess the security of your MLOps pipeline before deployment, identifying potential backdoors or vulnerabilities.

Quick Start

Run the ml-pipeline-security-review skill to analyze the security of your MLOps pipeline.