AWS IAM Policy Configuration Profiles Dataset for Cloud Misconfiguration Detection

This dataset contains 1,000 synthetic Amazon Web Services (AWS) Identity and Access Management (IAM) policy configuration profiles designed for machine learning research on cloud security misconfiguration detection. Each profile is labeled as either compliant or misconfigured, enabling binary classification, while misconfigured samples also include multi-label vulnerability annotations for vulnerability diagnosis. Each record contains a valid AWS IAM policy (Version 2012-10-17) together with IAM context such as entity type (user or role), deployment environment, MFA status, access key age, and policy attachment type. A flattened CSV version provides 21 deterministic numeric and binary features extracted from the policy, making the dataset directly usable for conventional machine learning algorithms. The dataset is intentionally imbalanced, containing 700 compliant (70%) and 300 misconfigured (30%) policies to approximate real-world cloud environments where insecure configurations are less common. Misconfigured policies include one to three injected vulnerabilities selected from 11 common IAM security weaknesses: full administrative access, wildcard actions, wildcard resources, public principals, unrestricted iam:PassRole, privilege-escalation action sets, missing MFA on privileged operations, missing encryption enforcement, removed conditional guardrails, cross-account trust without ExternalId, and over-permissive NotAction grants. 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.