AWS IAM Policy Configuration Profiles Dataset for Cloud Misconfiguration Detection

The dataset was generated using a template-based synthesis framework with controlled vulnerability injection. Secure policies follow the principle of least privilege, while misconfigured policies are created by injecting one to three vulnerabilities into compliant templates. This approach ensures realistic policy structures and accurate, noise-free ground-truth labels.

The dataset includes three files: (1) a JSON file containing complete IAM policy documents and metadata for text-based or sequence-based learning, (2) a CSV file containing the flattened feature matrix for traditional ML models, and (3) a summary JSON describing class distribution, vulnerability statistics, and feature definitions. (4) a genrate_aim_dataset.py script (5) documentation of the dataset description and the dataset production process The accompanying Python generator script, implemented entirely with the Python standard library, is fully seed-controlled, allowing byte-for-byte regeneration or extension of the dataset to different sizes and class distributions.

The dataset includes comprehensive documentation covering the data dictionary, generation process, vulnerability injection, and feature extraction. As all records are synthetic, no real AWS credentials or sensitive information is included, making the dataset safe for research, benchmarking, and educational use.