makabaga123

makabaga123 / aegis-native

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A multi-agent cloud-native security platform for Docker, Kubernetes, IaC, cloud config and runtime eBPF event analysis.

10
0
85% credibility
Found May 25, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AegisNative is a cloud-native security platform that helps DevSecOps teams find and fix risks in their container and cloud infrastructure. You upload your code and configuration files, and a team of AI agents automatically reviews them for security issues. The platform connects separate findings into coherent attack stories and prioritizes fixes so you know exactly what to address first. It can also connect to live security monitoring systems to catch suspicious behavior as it happens. Results are presented through a clean web dashboard with ranked findings, clear explanations, and actionable remediation steps. The platform supports scanning Dockerfiles, Kubernetes configurations, Terraform cloud code, and monitoring running containers using industry-standard eBPF security tools.

How It Works

1
🔍 You upload your cloud infrastructure files

You drop your Dockerfile, Kubernetes configuration, or Terraform code into the platform for review.

2
🤖 A team of AI security agents gets to work

Instead of one scanner, specialized agents each review their area — containers, cloud settings, running processes — like a security audit team.

3
🔗 Findings are connected into attack stories

The platform draws lines between separate issues to show you the full risk picture — not just individual warnings.

4
You can either review findings or connect live monitoring
📊
Review a detailed security report

See a ranked list of risks with clear explanations and step-by-step fix instructions.

📡
Connect to live security sensors

Keep monitoring your systems for suspicious activity like shell access, unauthorized connections, or cryptocurrency mining.

5
✅ You receive prioritized fix recommendations

Risks are ranked by severity, and each one comes with plain-language guidance on what to fix first.

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AI-Generated Review

What is aegis-native?

This is a Python-based cloud-native security platform that throws multiple specialized AI agents at your infrastructure to find problems. One agent audits Dockerfiles for misconfigs, another scans Kubernetes YAML for RBAC holes and privileged containers, another checks Terraform for public exposure and hardcoded secrets. The supervisor orchestrates everything, correlates findings across layers, and spits out ranked attack paths with remediation suggestions. It also hooks into runtime eBPF events from Falco or Tetragon to catch suspicious container behavior at execution time. You get a FastAPI backend, a React dashboard, and protocol-compliant APIs that speak standard JSON-RPC.

Why is it gaining traction?

The hook is the protocol compliance. It implements Google's A2A and Anthropic's MCP standards, which means you can wire it into Claude Desktop, Continue, or Cursor as a backend security scanner. The local-first design is clever too: it runs zero-dependency with local rules out of the box, no API keys required, so you can demo it immediately. Flip an environment variable and swap in DeepSeek, GPT-4, or Ollama for actual LLM reasoning when you're ready.

Who should use this?

DevSecOps engineers building internal CNAPP-style tools, security teams evaluating multi-agent orchestration patterns, and developers who want IDE-integratedIaC scanning without leaving their workflow. Also useful as a reference implementation if you're building MCP or A2A clients and need working server code to study.

Verdict

The 0.85 credibility score reflects well-structured code and clear documentation, but 10 stars means this is early-stage and community traction is unproven. Worth evaluating as a learning project or security add-on, but vet it thoroughly before trusting it in production. The protocol implementations are the real value here—if you need the standards in action, this is cleaner than most alternatives.

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