> For the complete documentation index, see [llms.txt](https://docs.salescaling.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.salescaling.com/en/compliance-and-legal/seguridad.md). # Security At Salescaling, data security and privacy are fundamental. This document details the security measures implemented and how we handle our users' information. ## AI Data Processing ### Current AI Model * We use Google Gemini as our Generative AI LLM model * Google's privacy policy applies * Google does not use input data to train its models * All information travels encrypted in transit until it reaches the LLM ### Future Improvements * We are developing the option for customers to use their own AI model if they wish * This will allow absolute control over information processing * Customers will be able to choose the model that best suits their privacy needs ## Security Measures ### Data Encryption * All communications are encrypted using TLS * Data stored in the database is encrypted at rest using AES-256 * Daily backups are encrypted and retained for 14 days ### Authentication and Authorization * We use Auth0 as the authentication platform * We do not store passwords * Permissions are managed based on the role within each organization ### Data Isolation * Data is segmented at the database level * Each organization can only access its own data ### Monitoring and Control * We monitor access logs * We implement alerts for sensitive operations * We track user activity for debugging * We maintain a detailed record of all critical operations ### Infrastructure * We use cloud infrastructure * We apply least-privilege policies * Access is restricted to authorized personnel only ## Support If you have questions about security, please contact our support team. ## Vulnerability Detection If you find a vulnerability, please contact our security team at . --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.salescaling.com/en/compliance-and-legal/seguridad.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.