Why We Built Stratusec: Cloud Security Needs AI-Native Tools

Cloud security in 2025 was broken. Not because the tools were bad — existing open-source scanners and cloud auditing tools are genuinely good software. But they were built for a world that no longer exists.

They were built for a world where security engineers manually review findings, where remediation means opening a Jira ticket and waiting three sprints, and where "AI integration" means a chatbot that summarizes your dashboard.

We built Stratusec because we believe cloud security needs to be reimagined for the AI era. Here's why.

The Problem With Finding Lists

Every cloud security tool works roughly the same way: connect to your cloud, run checks, produce a list of findings sorted by severity. Critical, High, Medium, Low. Maybe some compliance mapping. Maybe a PDF report for your auditor.

The problem isn't finding things. Modern CSPMs find plenty. The problem is that a flat list of 800 findings doesn't tell you anything actionable. Which of these 47 "High" severity findings actually matter? Which ones chain together into a real attack path? Which ones are already mitigated by other controls?

Security teams drown in findings. The average enterprise cloud account has hundreds of misconfigurations at any given time. Most are noise. A few are catastrophic. And the tools that find them can't tell you which is which.

Why Graphs Change Everything

When we started building Stratusec, the first architectural decision was the most important: every cloud resource goes into a Neo4j graph database.

Resources are nodes. Relationships — network paths, IAM permissions, data flows, trust relationships — are edges. Instead of checking resources individually, we analyze the graph.

This means Stratusec doesn't just tell you "this security group allows SSH from 0.0.0.0/0." It tells you: "This security group allows SSH from the internet to an EC2 instance that has an IAM role with access to your production database, and that database has unencrypted PII. Here's the full attack path."

That's not a medium-severity misconfiguration. That's a breach waiting to happen. And without graph analysis, you can't see it.

Commercial CNAPP vendors figured this out early and built billion-dollar companies on cloud security graphs. But they charge $50K+/year and the software is proprietary. We believe graph-based security analysis should be open source and free.

Why AI-Native, Not AI-Bolted

Every security vendor added "AI" to their marketing in 2024-2025. Most of it means "we put a ChatGPT wrapper on our API." That's not AI-native. That's AI-adjacent.

AI-native means the tool is designed from the ground up for AI agents to operate. Specifically, it means implementing the Model Context Protocol (MCP) — the open standard for AI agent-to-tool communication.

When Stratusec exposes an MCP server, your AI assistant doesn't just summarize findings. It can:

• Trigger scans and interpret results
• Query the attack path graph
• Generate and validate remediation code
• Check compliance status
• Write and test guardrail policies

The AI agent has the same capabilities as a human security engineer using the CLI. That's AI-native.

We built this because we believe the primary interface for security tools in 2027 won't be a dashboard. It'll be a conversation with an AI agent that has real tools, not just training data.

Why Open Source

We could have built Stratusec as a SaaS product with a 14-day free trial. We chose open source for three reasons:

1. Security tools should be transparent. If software is auditing your cloud infrastructure, you should be able to read every line of code. "Trust us, our scanner is secure" isn't good enough. Apache 2.0 means you can verify it yourself.

2. Community makes security better. Security checks written by one team in one context will miss things. A community of hundreds of contributors, each with different cloud environments and threat models, builds a more comprehensive tool.

3. Every team deserves cloud security. A startup with 3 engineers and no security budget deserves the same tools as a Fortune 500 with a $2M security vendor contract. Open source makes that possible.

The enterprise features (SSO, RBAC, advanced compliance, air-gapped deployment) are paid. The core — scanning, graph analysis, guardrails, MCP integration, auto-remediation — is free and always will be.

What's Different About Stratusec

In a crowded market, here's what Stratusec does that others don't:

We're not trying to replace every security tool. We're building the one we wished existed: a single platform that scans, understands relationships, prevents misconfigurations, fixes what it finds, and works with AI agents natively.

What's Next

Stratusec is early. The core works — scanning, graph analysis, guardrails, remediation, MCP, dashboard. But there's a lot more to build:

• Runtime detection — catching threats in real time, not just point-in-time scanning
• Multi-tenant — manage dozens of cloud accounts from one instance
• Custom compliance frameworks — build your own frameworks, not just the standards
• AI-powered policy generation — describe what you want in English, get OPA/Rego policies
• Attack simulation — use the graph to simulate attacks, not just find paths

We're building in public. The roadmap is on GitHub. Contributions are welcome.

If you believe cloud security should be open, AI-native, and accessible to every team — try Stratusec. Star the repo. Open an issue. Submit a PR. We're building this together.

git clone https://github.com/stratusecai/stratusec.git
cd stratusec
docker compose up -d

Open http://localhost:3001. You'll be scanning in under 5 minutes.