> For the complete documentation index, see [llms.txt](https://docs.avidml.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.avidml.org/taxonomy/introduction.md). # Introduction This section describes AVID's taxonomy framework as part of AVID's broader taxonomy library. AVID's primary focus is the database of GPAI failure evidence, while taxonomies are used to classify and query those records. The AVID taxonomy is intended to serve as a common foundation for AI engineering, product, and policy teams to manage potential risks at different stages of a GPAI workflow. In spirit, this taxonomy is analogous to [MITRE ATT\&CK](https://attack.mitre.org) for cybersecurity vulnerabilities, and [MITRE ATLAS](https://atlas.mitre.org/) for adversarial attacks on ML systems. At a high level, the current AVID taxonomy consists of two views, intended to facilitate the work of two different user personas. * [Effect view](/taxonomy/effect-sep-view.md): for the auditor persona aiming to assess risks for a GPAI system and its components. * [Lifecycle view](/taxonomy/lifecycle-view.md): for the developer persona aiming to build an end-to-end GPAI system while being cognizant of potential risks. Based on case-specific needs, people involved with building a GPAI system may need to operate as either of the above personas. > For machine-readability, taxonomies are shared using the standardized [MISP](https://www.circl.lu/doc/misp-taxonomies/) format. This enables support for additional taxonomies in the AVID taxonomy library. See [Schema](/taxonomy/schema.md) to learn more. --- # 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.avidml.org/taxonomy/introduction.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.