> For the complete documentation index, see [llms.txt](https://docs.aquilax.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aquilax.ai/user-manual/scanners/secret-scanning.md). # Secret Scanning Secret Scanning is a vital security measure that detects confidential data, such as API keys, passwords, and tokens, that may accidentally get exposed in your code repositories. These secrets, if leaked, can give malicious attackers direct access to your services, databases, or cloud resources, leading to data breaches or financial losses. AquilaX AI’s secret scanning leverages machine learning (ML) algorithms to continuously monitor your codebase for these sensitive secrets. By using contextualized analysis, it eliminates false positives and ensures that real risks are flagged promptly. Protecting these secrets is critical for securing your organization’s infrastructure and preventing unauthorized access to your sensitive systems. **Why Secret Scanning Matters:** • Prevent unauthorized access to systems. • Avoid costly data breaches. • Protect sensitive user and business information. --- # 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.aquilax.ai/user-manual/scanners/secret-scanning.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.