stratus-ai - AI-powered AWS and GCP cloud security scanner with

项目说明

StratusAI

Multi-cloud AI security scanner that produces AWS, GCP, and external exposure findings with attack-chain synthesis in about 2-4 minutes per scan.

!License
!Last commit
!Python
![External submissions]()
![Accepted upstream]()

Demo

Add an 8-15 second GIF showing: wizard start -> cloud target selection -> findings -> AI attack-chain summary.

What This Is For

A cloud-security engineer runs StratusAI to get quick exposure context across AWS, GCP, and external surfaces. It is designed for prioritized findings, not raw scanner noise.

What It Produces

| Output | Use |
|---|---|
| Findings report | Cloud review |
| Attack-chain synthesis | Prioritization |
| JSON output | Automation |
| Deployment wizard | ECS Fargate or Cloud Run Job |
| Test suite | Regression confidence |

Quick Start

How It Works

Co

| Area | Co |
|---|---|
| AWS | 9 modules |
| GCP | 7 modules |
| External | 4 modules |
| AI pro | Claude, GPT-4o, Gemini |
| CI | 125-test suite; zero li calls in CI |
| Cost | About $0.01-$0.15 per scan, depending on model |

Limitations And Honesty

StratusAI is a fast prioritization layer. It should not replace CSPM, cloud logs, manual review, or organization-specific policy .

Companion Article

Citation

See CITATION.cff.

License

MIT

Kali Linux Submission

The core package supports external assessment and --no-ai operation without
an AI API key. AWS, GCP, and AI integrations remain optional. Debian/Kali
package metadata is under debian/, with a complete
New Tool Request draft.

Security Policy

See SECURITY.md.

1200km Ecosystem

This project is part of the 1200km security research ecosystem. Use Ad for CTI-to-detection workflows, ATT&CK/ATLAS mapping, actor relevance, IOC enrichment, and analyst-ready reporting.

项目信息

开发语言：Python

Star 数：3

技术要点

该项目属于防护思路方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕AI-powered AWS and GCP cloud security scanner with attack-chain synthesis, deploy wizard, and test coverage.展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。