i've been building an open-source AI Security Posture Management tool for the past 3 months, and just shipped a major infrastructure upgrade

This isn't sponsored or affiliated with anyone, just sharing what I've been working on and what changed in this release

Also if you need any help with AI security or similar projects, feel free to DM me

Figured I'd put this together in case someone else is working on enterprise AI security tooling or thinking about production-grade Kubernetes setups

What is AI-SPM?

AI-SPM (AI Security Posture Management) is an open-source, enterprise-grade platform I've been building to help organizations proactively protect their AI systems from threats, minimize data exposure, and maintain the trustworthiness of their AI applications

It covers the full AI stack: models, agents, MCP servers, data pipelines, and more

The goal is to give security teams continuous visibility and control over their AI infrastructure—kind of like CSPM (Cloud Security Posture Management) but specifically designed for AI workloads

The Problem: Single-Node Dev Environment

Up until this release, the dev environment was running on a single-node kind cluster

That worked fine for basic development, but it had real limitations:

• couldn't realistically test multi-node Kubernetes scenarios
• no way to validate HA (High Availability) behaviors
• dev environment didn't match what production would actually look like
• made it harder to catch issues that only show up in distributed setups

Basically, I was building an enterprise tool but testing it in a setup that didn't reflect enterprise reality

What Changed in This Release

This release moves dev from a single-node kind cluster to a production-shaped HA topology that mirrors the prod target one-for-one

Here's what that looks like now:

• 3 control-plane Kubernetes nodes running on Docker Desktop via kind
• No worker nodes — control-plane taints lifted on dev so application pods can schedule cluster-wide
• Dev environment now matches production setup exactly

It's still running locally via kind, so you don't need a full cloud setup to develop or test, but the topology is production-grade

What Stands Out (and Why It Matters)

You can now test realistic multi-node Kubernetes scenarios without needing full production infrastructure

• HA failover behaviors are testable locally
• Multi-node orchestration works the same way it will in production
• You can validate etcd quorum, control-plane redundancy, and distributed workload scheduling

Dev environment matches production setup exactly

• What you test locally works in production
• No surprises when you deploy
• Reduces the gap between development and production environments

Shows the project is maturing toward production-readiness

• This isn't just a proof-of-concept anymore
• The infrastructure is built to handle real enterprise workloads
• HA topology demonstrates commitment to reliability and quality

Still accessible for local development

• Runs on Docker Desktop via kind
• You don't need a cloud account or expensive infrastructure to contribute
• Fast iteration cycles with production-grade architecture

Try It Out

If you're working on AI security, Kubernetes tooling, or just curious about AI-SPM, I'd love to hear your feedback

Contributions are welcome, and if you run into any issues or have questions about the setup, feel free to open an issue or DM me

Also happy to help if you're working on similar projects or trying to figure out production-grade dev environments for your own tools