Cyber Security
& Infrastructure
A two-track collaboration — application security testing, and a new generation of decentralized, self-hosted infrastructure.
Two tracks.
Evaluated separately. Delivered together. Click through to any system below.
Cyber Security
Infrastructure
Penetration Testing
In Stage 1, we do complete research on your system — mapping every endpoint it has, before touching a single one of them.
| Endpoint | Method | Input | Output | Access |
|---|---|---|---|---|
| /api/auth/login | POST | username, password | session token | Public |
| /api/citizen/records | GET | citizen ID | name, address, national ID | Authenticated |
| /api/payments/verify | POST | transaction ID, amount | verification status | Authenticated |
| /api/documents/upload | POST | file, metadata | document ID | Authenticated |
| /api/admin/users | GET | admin token | full user list | Admin |
Illustrative recon output — actual scope and endpoints defined per engagement.
Next Non-Destructive Attack — testing what the map found. Continue →Non-Destructive Attack
With the map in hand, we test whether access is actually possible — without breaking anything. Tampering with inputs to reach a server shell. Uploading a file built to run code. Sending malformed requests just to see where the system throws an error.
Destructive: where the system gets fragile.
Here, we're dealing with things that can actually take down a live system — database records corrupted until the system gets stuck throwing errors, other users' data getting leaked, or a brute-force attack at mass scale like how governmental attacks happen: real computing power from nodes worldwide, all aimed at cracking password hashes.
You can't prove a system survives damage without causing it.
This is exactly why sandboxing is so important. The application gets redeployed on its own separate server, built to match production exactly — and every destructive or large-scale test happens there, and only there. Production is never touched without written approval.
No two hackers see the same system.
Every researcher looks at a system differently — a different angle, a different entry point, a different way of spotting where a flaw might be hiding. That's why, after staged testing is finished and the fixes are in place, we'd recommend opening a bug bounty program through a platform like HackerOne. It keeps fresh eyes on the system, all the time.
Real activity, HackerOne Hacktivity feed.
Some of the most secure companies in the world still run programs like this every day — not because their own testing failed, but because a zero-day is simply a flaw nobody had spotted yet. Every report makes the system stronger, so it keeps getting harder to break — not just once, but continuously.
Our cyber security products.
Real-Time Packet Monitoring
Every packet crossing the network gets read the instant it happens — not sampled, not batched, not reviewed after the fact. An AI model built for anomaly detection analyzes each one in real time, learning what normal traffic on your network looks like, so it can flag the moment something doesn't.
We also have a tool that shapeshifts data to bypass any packet monitoring system.
Honeypot Decoy System
We plant a known vulnerability inside the decoy on purpose — so we can watch whoever finds it. An attacker who exploits it thinks they've broken into the real server. But what they've actually landed on is the honeypot underneath — and from that point on, we see everything they do.
New-Generation Datacenters
Now, this is where it gets interesting.
We build the next generation of data centers. Instead of one big server, we use mini PCs connected through the mesh network with distributed storage, making sure there's always a backup of your data on other nodes.
The software itself is super light and optimized to run on these small mini PCs instead of expensive enterprise servers. That one choice alone makes a huge difference in power usage.
Traditional data centers are power-hungry by design — packed racks and a lot of cooling. Ours works the opposite way: spread across different physical locations, like each office or station, automatically connected through the mesh network — with no dedicated cooling system needed at all.
Every node also connects to every other node automatically, with no manual setup — no virtual machine ever gets its own IP address, and every connection is zero-trust by default.
Every node connects to every other node automatically — no manual setup, no single point of failure.
| Platform | Licensing | Mesh Networking | Distributed Storage | Hardware Class |
|---|---|---|---|---|
| VMware / Hyper-V | Per-socket / core | Not native | Add-on | Enterprise servers |
| Proxmox | Subscription tiers | Manual config | Manual config | Enterprise / commodity |
| OpenStack | Free / support-paid | Complex setup | Ceph, separate stack | Enterprise servers |
| Harvester | Free, open-source | Not native | Longhorn, separate stack | Enterprise servers |
| Our Platform | No licensing cost | Native, built-in | Native, built-in | Low-power mini PCs |
NetCtl. A self-hosted alternative to ngrok — built on an SSH-secure base.
NetCtl is our alternative to ngrok — installed directly on each VM, opening any HTTP or TCP port straight to the internet without exposing the VM itself. No third-party control plane. No per-seat licensing. The connection stays zero-trust by design, and there's no public IP to assign, manage, or pay for per VM. Traffic and metadata stay entirely under Türksat's control.
A new-generation datacenter system.
Not VMware. Not OpenStack.
Built for the distribution.
Native KVM virtualization for the best performance, with automatic failover if any node goes down — mesh networking and distributed storage built in, not bolted on.
Cheaper to buy. More efficient to run.
A mini PC today delivers more compute per watt than an enterprise rack server, at a fraction of the price. Spread across separate physical locations, each node produces so little heat that no dedicated cooling is needed anywhere in the system. It's the decentralization itself that removes the cooling bill — the efficient chips are just a bonus.
Let's compare the 1 MW datacenter — same power budget, two ways to build it.
Setup A: a traditional rack room with enterprise servers and dedicated cooling. Setup B: the same 1MW budget spread across low-power mesh nodes. This is 100% CPU compute — no AI/GPU allocation included.
| Metric | Setup A — Traditional | Setup B — Decentralized | Difference |
|---|---|---|---|
| Total machines | 1,040 | 46,942 | 45× more units |
| Total CPU cores | 21,088 | 253,272 | 12× more cores |
| Cooling required | 250 kW dedicated | None | Eliminated |
| Hardware cost per core | ~$127 | ~$59 | 53.7% less money |
| Power draw (same performance) | 667.4 kW | 76.4 kW | 88.6% less |
Power draw row is matched at 21,088 cores on both sides — not the full 46,942-machine buildout shown above.
AI datacenters, with a low-power cluster.
AI datacenters draw enormous power — GPU hardware like the NVIDIA HGX H200 costs the most and draws the most. We use NPU-based, AI-optimized nodes instead — the same MINISFORUM hardware already priced into this proposal, self-hosted, with nothing leaving your network.
The GPU bill. Or the power bill.
With four of these nodes clustered together, there's enough performance to run DeepSeek-R1 671B at Q4 quantization — entirely on-premises, no cloud dependency. Add more nodes, get more power, and the same cluster becomes the core AI system for anything that needs it.
This is the core API for anything you plug into it.
Satellite Imagery
Document Intelligence
Security Firewall Analyze
Citizen Services Assistant
Camera Alert System
Whatever's Next
Is AI going to be dangerous in the future?
AI is already part of daily life for billions of people — in search, at work, in everyday tools. That isn't changing. But running it takes far more power than running a regular datacenter. And almost all of that power comes back out as heat. Immediately, not eventually.
This isn't science fiction. It's already real. Every AI server generates heat, and that heat is part of the same warming pattern already reshaping the planet. In recent years, both the Danube and the Po rivers have repeatedly dropped to record lows, while wildfires swept across France and Spain. Heat waves now kill hundreds of thousands of people every year, worldwide, and that number keeps climbing. AI isn't the whole reason this is happening — but right now, it's one of the fastest-growing pieces of it. Every bit of energy a computer uses has to go somewhere. In the end, all of it becomes heat.
Source: ~546,000 average annual global heat-related deaths — Lancet Countdown / WHO, 2025. AI datacenter electricity growth — International Energy Agency (IEA), 2026.
This is exactly why the infrastructure in this proposal matters. It uses a fraction of the power a normal AI setup would. It needs no extra power for cooling. And the less power we use, the less heat we generate.
Thank you for your time.
This proposal, and everything in it, lives at the link below — always available to revisit.