> For the complete documentation index, see [llms.txt](https://docs.owkin.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.owkin.com/governance-and-security/privacy-architecture.md). # Privacy architecture K Pro is designed with privacy and security at its core to protect your data: * **Personal chat history:** Your chat history is securely stored and always associated with your user account and organization * **Access control:** Only authenticated users can access their own chat history - no one else can view your conversations * **Secure infrastructure:** Our database is hosted on a managed, secure cloud infrastructure with strict access controls and network policies * **Data isolation:** Data uploaded to K Pro is only visible to you and members of your organization Owkin's platform enforces data boundaries through a multi-account architecture, ensuring that each customer's data is isolated from others. Effectively, each instance of K Pro is a single-tenant deployment in an isolated account. Owkin's platform enforces centralized authorization checks before any dataset/tool access using Role-Based Access Control (RBAC). Customer data is accessed only through Owkin K services/tools (query-based + APIs/SDK); end users do not get direct DB access. *** K Pro draws on several categories of knowledge sources to contextualize responses to users: * **Underlying data assets analyzed by K Pro.** Held either in secured object storage or in connected data platforms (Snowflake, Databricks, etc.). The data model varies by asset and is preserved as-is from the source system; K Pro accesses these through purpose-built connectors rather than re-modeling the data centrally. * **User information.** Stored in a relational database (RDBMS), capturing identity and account attributes such as name, email, and organization. * **Interaction logs.** Logged in a relational database (RDBMS). Each entry records the user question, tool calls invoked, latency, timestamp, and supporting metadata required for observability and auditability. * **Evaluation and observability telemetry.** Streamed to dedicated observability and evaluation platforms, where logs and traces support quality monitoring, regression testing, and incident investigation. K Pro does not currently maintain a central knowledge graph / vector store as part of the core data model. User traces can be accessed programmatically via a backend API to support observability. This API feeds the observability platform. --- # 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.owkin.com/governance-and-security/privacy-architecture.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.