--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: - "catalog.json" --- # Governance Studio Frameworks This repository provides configurations for Shift Left Governance Studio Frameworks. These frameworks are designed to ensure quality control and effective governance across various projects. Follow the instructions below to set up and import a framework. ## Table of Contents - [Prerequisites](#prerequisites) - [Setup Instructions](#setup-instructions) - [1. Create Directory](#1-create-directory) - [2. Create frameworks.json File](#2-create-frameworksjson-file) - [3. Fill in the JSON](#3-fill-in-the-json) - [4. Import to Dashboard](#4-import-to-dashboard) - [Example frameworks.json](#example-frameworksjson) ## Prerequisites Ensure you have access to the Governance Studio dashboard for importing frameworks. ## Setup Instructions ### 1. Create Directory First, create a directory to store your framework configuration files. It's a good practice to name this directory using the framework's short name for easy identification. For example, if your framework is "Databricks AI Security Framework" with the short name "DASF", you should name the directory `DASF`. ```bash mkdir cd ``` ### 2. Create frameworks.json File Inside the directory you created, create a file named `frameworks.json`. ```bash touch frameworks.json ``` ### 3. Fill in the JSON Open the `frameworks.json` file in your favorite text editor and paste the following JSON structure into the file. Customize the JSON to define your specific framework, including stages, system components, risks, and controls. ```json { "name":"Your Framework Name", "shortName":"Your-Short-Name", "description":"Description of your framework", "iconUrl":"https://your-icon-url.png", "stages":[ { "stageName":"Stage Example", "systemComponents":[ { "componentName":"Component Example", "risks":[ { "riskId":"1.1", "title":"Example risk title", "definition":"Example risk definition.", "addressedByControls":["Control-1"] } // Add more risks as needed ] } // Add more system components as needed ] } // Add more stages as needed ], "controls":[ { "controlId":"Control-1", "title":"Example control title", "description":"Example control description.", "controlCategory":"Your Category", "readableControlId":"Control-1", "severity":"medium", "automationPlatforms":[], "criteria":[] } // Add more controls as needed ] } ``` ### 4. Import to Dashboard Once you have created and filled out the `frameworks.json` file, you can import it to the dashboard by following these steps: 1. Open the Governance Studio dashboard in your browser. 2. Navigate to the **Frameworks and Regulations** section. 3. Click on **Import**. 4. Follow the on-screen instructions to complete the import process. 5. The framework has now been imported and can be applied to a workspace. ## Example frameworks.json Below is an example structure for quick reference. Customize it according to your specific framework needs. ```json { "name":"Databricks AI Security Framework (DASF)", "shortName":"DASF", "description":"The Databricks AI Security Framework (DASF) is a comprehensive guide developed by the Databricks Security team to help organizations understand and mitigate the evolving security risks associated with the widespread integration of artificial intelligence (AI) systems. Unlike approaches that focus solely on securing models or endpoints, the DASF adopts a holistic strategy to address cyber risks across all components of an AI system. The framework is designed to facilitate collaboration between business, IT, data, AI, and security teams throughout the AI lifecycle. It provides actionable defensive control recommendations that can be updated as new risks emerge and additional controls become available. The DASF walks readers through the 12 foundational components of a generic data-centric AI system, detailing 55 identified technical security risks and dedicated controls to mitigate those risks. It also includes a guide on how to manage and deploy AI models safely and securely using the Databricks Data Intelligence Platform. The framework aims to be a valuable resource for security teams, ML practitioners, and governance officers to gain insights into AI system security, apply security engineering principles to ML, and access a detailed guide for understanding the security and compliance of specific ML systems.", "iconUrl":"https://asset.brandfetch.io/idSUrLOWbH/idxN8rZ-u3.png", "stages":[ { "stageName":"Data Operations", "systemComponents":[ { "componentName":"Raw Data", "risks":[ { "riskId":"1.1", "title":"Insufficient access controls", "definition":"Effective access management is fundamental to data security, ensuring only authorized individuals or groups can access specific datasets. Such security protocols encompass authentication, authorization and finely tuned access controls tailored to the scope of access required by each user, down to the file or record level. Establishing definitive governance policies for data access is imperative in response to the heightened risks from data breaches and regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These policies guard against unauthorized use and are a cornerstone of preserving data integrity and maintaining customer trust.", "addressedByControls":["DASF-1"] } ] } ] } ], "controls":[ { "controlId":"DASF-1", "title":"SSO with IdP and MFA", "description":"Implementing single sign-on with an identity provider\u2019s (IdP) multi-factor authentication is critical for secure authentication. It adds an extra layer of security, ensuring that only authorized users access the Databricks Platform.", "controlCategory":"Configuration", "readableControlId":"DASF-1", "severity":"medium", "automationPlatforms":[], "criteria":[] } ] } ```