azure-mgmt-policyinsights

v1.0.1 safe
3.0
Low Risk

Microsoft Azure Policyinsights Management Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk indicators across all categories except metadata, where incomplete author information raises minor concerns. There are no signs of malicious activity.

  • Low network and shell execution risks.
  • No evidence of obfuscation or credential harvesting.
  • Incomplete author metadata.
Per-check LLM notes
  • Network: No network calls detected, which is unusual but not necessarily indicative of malicious activity for a management package.
  • Shell: No shell execution patterns detected, aligning with the expected behavior for a legitimate Python package.
  • Obfuscation: The observed pattern is likely a standard method to extend module search paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author information is incomplete, which raises some concern, but there are no other red flags.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: conftest.py
  • 7 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (11227 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 239 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

Outbound Network Calls

No suspicious network call patterns found

Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore __path__ =
  • ) # type: ignore __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # coding=u
Shell / Subprocess Execution

No shell execution patterns detected

Credential Harvesting

No credential harvesting patterns detected

Typosquatting

No typosquatting candidates detected

Registered Email Domain

Email domain looks legitimate: microsoft.com> license-expression: mit

Suspicious Page Links

All external links appear legitimate

Git Repository History

Repository Azure/azure-sdk-for-python appears legitimate

Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with azure-mgmt-policyinsights
Create a Python-based utility that leverages the 'azure-mgmt-policyinsights' library to manage and analyze compliance policies within a Microsoft Azure environment. This tool will serve as a powerful assistant for DevOps engineers and cloud administrators to ensure their resources adhere to organizational standards and regulatory requirements.

### Core Functionality:
1. **Policy Compliance Check**: Implement a feature that allows users to specify one or more Azure resource groups or subscriptions and retrieve their compliance status against all assigned policies. This should include details such as the policy definition ID, effect, and any non-compliant resources identified.
2. **Policy Definition Retrieval**: Enable users to fetch detailed information about specific policy definitions, including their description, metadata, and parameters. This will help in understanding the criteria against which resources are being evaluated.
3. **Compliance Alerts**: Integrate a mechanism to set up alerts based on policy compliance statuses. Users should be able to define thresholds (e.g., if more than X% of resources are non-compliant) and receive notifications via email or webhook when these conditions are met.
4. **Policy Assignment Management**: Provide functionality to create, update, and delete policy assignments at the subscription or management group level. This includes specifying the scope, policy definition ID, and parameters.
5. **Custom Reports Generation**: Allow users to generate custom reports summarizing compliance across multiple scopes, including visualizations like pie charts or bar graphs to highlight compliance trends over time.

### Additional Features:
- **User Authentication**: Utilize Azure Active Directory for secure authentication, ensuring only authorized users can access and modify policy configurations.
- **Command Line Interface (CLI)**: Develop a CLI interface that simplifies interaction with the tool, allowing users to perform common tasks without needing to write code.
- **Integration with CI/CD Pipelines**: Offer documentation and examples on how to integrate this utility into existing CI/CD pipelines to automate compliance checks during deployment processes.
- **Real-Time Monitoring**: Extend the alerting system to provide real-time monitoring capabilities, sending immediate notifications whenever a policy compliance status changes.

### How 'azure-mgmt-policyinsights' is Utilized:
- **Client Initialization**: Use the 'PolicyInsightsClient' class from 'azure.mgmt.policyinsights' to initialize a client object that will interact with Azure Policy APIs.
- **API Calls**: Leverage methods provided by the client object, such as 'query_policy_states', 'get_policy_definition', and 'create_or_update_policy_assignment', to execute the core functionalities of your utility.
- **Error Handling**: Implement robust error handling mechanisms to gracefully manage exceptions and provide meaningful feedback to users when API calls fail or return unexpected results.

This project aims to streamline Azure policy management, making it easier for teams to maintain compliance while reducing manual effort and potential human errors.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!