> For the complete documentation index, see [llms.txt](https://help.dscout.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.dscout.com/dscout-ai/what-is-dscout-ai/ai-privacy-and-security-in-dscout.md). # AI privacy and security in Dscout Dscout has taken measures to manage artificial intelligence (AI) cyber risk prior to releasing AI features. Our objective is to build trustworthy AI solutions with the appropriate security controls to protect your data. Trust guardrails and data protection measures have been implemented to achieve this objective. Industry standards such as ISO 42001 and NIST AI RMF (Risk Management Framework) inform our approach to AI risk management. Below, you'll find a brief summary of steps we have implemented in order to deliver responsible AI features. ## Trust guardrails In order to build trust prior to releasing new AI features, we use the following guardrails: * Minimize bias in AI output. * Continually assess and adjust models to increase accuracy. * Explicitly label AI outputs as AI-generated to provide transparency. ## Data protection measures Security is embedded into AI features to protect against relevant threats. Dscout follows guidance provided by OWASP Top 10 for Large Language Models (LLMs) as we build AI features that leverage LLMs. Here are some things you should know about our data protection measures: * Third party AI provider APIs are secured and keys are properly managed. * All AI data is encrypted during transmission and at rest. * Data is not persistently stored in the environments of third party AI provider’s (for example, OpenAI). * Third party AI providers are restricted from using Dscout customer data to train their models. * Administrator access to the AI provider environment is restricted and strong authentication is required. We are committed to building secure AI features that can be trusted. Please contact us at if you have questions. --- # 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://help.dscout.com/dscout-ai/what-is-dscout-ai/ai-privacy-and-security-in-dscout.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.