# AI Models AI Models operate on terms that help define similar data objects. These terms facilitate easy control over data objects, allowing for the application of policies and standardization of metadata. Terms are analyzed for their applicability to various data objects. The AI models examine the metadata, data, and patterns of the target data objects to recommend and apply relevant terms and classifications. ## Use Case * **Security:** In a healthcare organization, the Data Classification Recommendation module can help to identify and secure patient records to comply with HIPAA regulations. It classifies records containing sensitive data, such as medical history, diagnoses, and treatments, enabling enhanced security measures. Users can utilize AI models to get recommendations tailored to financial data types, such as credit card numbers, bank account details, and transaction amounts. The bulk operations feature enables users to review and accept or reject recommendations efficiently. * **Literacy:** In multinational corporations, the Data Classification Recommendation module enhances employee data literacy across departments. For instance, marketing teams classify customer demographics and preferences, while legal teams classify contracts and agreements. ## Execution Methods Users can execute the AI Model for a whole domain, like Privacy, and receive recommendations for all PII terms. * Manual Execution or Scheduled Runs: Users can manually run AI Models to receive recommendations or schedule them to execute within specific time frames. * Model Modification: Users can edit and modify AI Models according to their requirements. * Viewing Recommendations: Within the AI Models, users can view recommendations on data objects, which they can do with a simple Yes/No. ## AI Model Logic The AI Models work in the backend based on Smart Score, which analyzes an object's Name, Data, and Pattern to determine the relevance of an object-term relation. The model starts by analyzing the characteristics of the data objects, including their name, metadata, and content. It then examines the patterns in the data, including the frequency and co-occurrence of different terms within the data objects. Once the Smart Score is calculated, the AI model generates a list of the most relevant data objects for the given term. ### Smart Score Parameters Smart Score displays scores based on four parameters: Name, Data, Patterns, and Data Types. * Name Score: It is calculated by matching the column's name with aggregated Names in the AI Model, which generates a score. A name in the model can be repeated more than once (Email:2); in such cases, some weight is added to that score. Finally, the maximum score among all is considered for the score. * Data Score: It is calculated by matching top values with aggregated top values in the AI Model. The top values are identified during the profiling process. * Data Types Match Boost - The recommendation model considers data types when recommending business glossary terms, improving accuracy. The system compares the data type of a data object (e.g., int, char, varchar) with the data types of objects already associated with a glossary term. If the data types match, the smart score for that term increases by +10. * Pattern Score: The pattern score is displayed based on the data patterns of the column compared to the ones existing in the AI Model. ### Access Control Access to the Data Classification Recommendation is handled through Application Security. Author license users present in the Authorized Roles section can access the Data Classification Recommendation Modules and create Models. Stewards of the term and data objects with access to the Data Classification Recommendation page can also visit the page and accept or reject the recommendations.