> For the complete documentation index, see [llms.txt](https://documentation.immuta.com/2024.2/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://documentation.immuta.com/2024.2/secure-your-data/secure-introduction/understandability.md). # Understandability ## Natural language represented policy This is a pretty simple one: if you can’t show your work, you are in a situation of trust with no way to verify. Writing code to enforce policy (Snowflake, Databricks, etc.) or building complex policies in Ranger does show your work to a certain extent - but not enough for outsiders to easily understand the policy goals and verify their accuracy, and certainly not to the non-engineering teams that care that policy enforcement is done correctly. With Immuta, policy is represented in natural language that is easily understood by all. This allows non-engineering users to verify that policy has been written correctly. Remember that when using global policies they leverage tags rather than physical table/column names, which further enhances understandability. Lastly, and as covered in the [scalability principle](/2024.2/secure-your-data/secure-introduction/scalability-and-evolvability.md), with Immuta you are able to build far fewer policies, upwards of 75x fewer policies, which provides an enormous amount of understandability with it. Certainly this does not mean you have to build every policy through our UI - data engineers can build automation through the Immuta API, if desired, and those policies are presented in a human readable form to the non-engineering teams that need to understand how policy is being enforced. ## Policy history and changes Understandability of policy is critically important. This should be further augmented by change history around policy, and being able to monitor and attribute change. Immuta provides this capability through extensive audit logs and takes it a step further by providing history views and diffs in the user interface. This is different from query activity in your data platform, as discovered and surfaced in Immuta Detect. In addition to that, actions taken in Immuta that alter policy decisions are audited and allowing the creation of compliance reports around that information. Without Immuta, if you build policy based on tasking an engineer in an ad-hoc manner there is no history of the change, nor is it possible to see the difference between the old and new policies. That makes it impossible to take a historical look at change and understand where an issue may have arisen. If you have a standardized platform for making policy changes, like Immuta, then you are able to understand and inspect those changes over time. --- # 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://documentation.immuta.com/2024.2/secure-your-data/secure-introduction/understandability.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.