# Dataset-Level VS Row-Level Security (/core/embed/permissions-and-row-level-security/dataset-level-vs-row-level-security) **Target Audience**: Non technical users & Developers ### TL;DR [#tldr] Toucan AI utilizes dataset-level security to restrict access to entire data sources and Row-Level Security (RLS) to filter specific records within a shared dataset. *** ### When to use this [#when-to-use-this] Use this page to determine the appropriate security layer for isolating data between different departments, teams, or external tenants. *** ### Dataset-level security [#dataset-level-security] Dataset-level security defines which users or roles are authorized to access a specific data source in its entirety. * **Definition**: This layer restricts access to the complete dataset; users without permission cannot query or visualize any part of the source. * **How it works**: Permissions are assigned at the dataset object level. Unauthorized users are unable to view the dataset within the Toucan AI interface. * **Use case**: Separating sensitive data by department, such as ensuring HR datasets remain private from the general Sales team. * **Example**: A "Finance" dataset is configured so it is only visible to the Finance group; no other users can interact with its metadata or values. *** ### Row-Level Security (RLS) [#row-level-security-rls] RLS provides a granular filtering mechanism that allows multiple users to access the same dataset while only viewing records relevant to them. * **Definition**: This layer filters data rows within a dataset based on dynamic user attributes. * **How it works**: Toucan AI injects filtering policies into the query at execution time, utilizing attributes passed in the authentication token. * **Use case**: Multi-tenant environments where all sales representatives use a single "Global Sales" dashboard but are restricted to their specific regional data. * **Example**: In a "Customer Orders" dataset, an employee with the attribute `region: France` only sees French orders, while a manager with broader attributes may see all regions. *** ### Comparison and selection [#comparison-and-selection] | Feature | Dataset-Level Security | Row-level Security (RLS) | | ---------------- | ----------------------------------------- | ------------------------------------------------------------- | | **Scope** | Entire dataset or data source. | Individual rows within a dataset. | | **Visibility** | Dataset is hidden if unauthorized. | Dataset is visible, but content is filtered. | | **Management** | Assigned to specific roles or teams. | Driven by dynamic user attributes (e.g., `department`, `ID`). | | **Primary Goal** | Strict boundaries between business units. | Personalized, multi-tenant data isolation. | *** ### Implementation best practices [#implementation-best-practices] * **Layered Security**: Start with dataset-level restrictions for broad access control, then apply RLS for fine-grained filtering. * **Single Source of Truth**: Use RLS to avoid duplicating datasets or dashboards for different user groups. * **Validation**: Test security policies with various user attribute combinations to ensure compliance and correct data isolation.