sensitive-data

Author: moohax

sensitive_data_extraction/README.md

@@ -4,27 +4,21 @@
 
 This agent leverages a Large Language Model (LLM) to autonomously explore and analyze file systems for sensitive data. It is designed to navigate through a given path, read the contents of various files, and identify information such as passwords, API keys, personal identifiable information (PII), and other confidential data. A key feature of this agent is ability to operate on a wide variety of storage systems, including local directories, cloud storage like AWS S3 and Google Cloud Storage, and even remote sources like GitHub repositories (via [fsspec](https://filesystem-spec.readthedocs.io/en/latest/)).
 
-## Intended Use
-
-The Agent is used to perform a thorough search through fileshares and files, then reporting its findings in a structured format, which can then be used for remediation efforts.
 
 ## Environment
 
-The environment is simply a filesystem. The Agent must have the necessary credentials to access the target path specified by the user (e.g., AWS credentials configured for S3 access, or a GitHub token for private repositories). For observability, the agent can be [connected to a Dreadnode server](https://docs.dreadnode.io/strikes/usage/config) to log detailed run information, metrics, and findings.
+The environment is simply a filesystem. The Agent must have the necessary credentials to access the target path specified by the user (e.g., AWS credentials configured for S3 access, or a GitHub token for private repositories).
 
 ## Tools
 
 - `fsspec`: The underlying library that provides a unified Pythonic interface to various local and remote file systems. This is what enables the agent's versatility in accessing different storage backends like `s3://`, `gs://`, and `github://`.
 
-## Features
-
-- **Multi-Filesystem Support**: Can analyze files on local disks, AWS S3, Google Cloud Storage, GitHub repositories, and any other backend supported by fsspec.
-- **LLM-Powered Data Identification**: Employs a language model to intelligently parse file contents and identify a broad range of sensitive data types based on context.
-- **Structured Data Reporting**: Uses a dedicated report_sensitive_data tool that forces the LLM to report findings in a structured format, including the file path, location within the file, data type, the sensitive value itself, and a comment.
-- **Location-Aware Reportin**g: Can specify the location of findings differently based on the file type (line number for text, seconds for audio/video, or byte offset for binary files).
-- **Autonomous Exploration**: The agent can independently navigate the directory structure of the target path to ensure comprehensive coverage.
-- **Task Contro**l: Includes tools for the agent to explicitly complete_task with a summary or give_up if it gets stuck, providing better insight into its reasoning process.
 
 ## References
 
 - [fsspec](https://github.com/fsspec/fsspec)
+
+
+## Examples
+
+`uv run main.py --model "" --path ""`