AI Cybersecurity for Critical Infrastructure
AI agents are entering operational technology, financial systems, and enterprise workflows. House of Galatine builds the control layer that decides what they're allowed to do — before they do it.
The company
House of Galatine is an AI cybersecurity company focused on one problem: controlling what AI agents are allowed to execute in real-world systems.
We build infrastructure that sits between AI and action. Our core product, MIG (Memory Intelligence Graph), is a deterministic execution control layer that validates every AI-driven command against policy before it reaches any system — whether that's a PLC in an oil plant, a database in a financial institution, or a hiring workflow in an enterprise.
We don't monitor what happened. We control what's allowed to happen.
Founded in 2024. Patent pending. Validated on live industrial control systems.
What we do
We build the product and help you deploy it. Whether you need the execution control layer or help building AI systems with governance built in.
The AI execution control layer. MIG sits between AI agents and the actions they take. Every command is validated against a policy graph before execution. Deterministic. Auditable. Fail-closed.
We help organizations deploy AI agents with governance built in from day one. Short-sprint engagements focused on safe, controlled AI implementation.
The problem
Last week, an AI coding agent wiped a startup's entire production database in 9 seconds. In OT environments, that's not a database — it's a turbine, a pipeline, a water treatment plant.
The agent decides what to do and does it. Policy compliance is a prompt instruction, not a structural enforcement. The agent can ignore it.
Actions happen. Nobody knows why they were allowed. There's no record of what was checked, what data was being carried, or what policy was applied.
When the system is uncertain, it proceeds anyway. Unknown becomes implicit permission. In critical infrastructure, that can be catastrophic.
How MIG works
Before any AI agent action executes, MIG intercepts it and runs it through a deterministic validation pipeline. The agent never decides its own permissions.
Every command receives one of three decisions:
Action verified against policy. Safe to execute. Proceeds to the target system. Decision logged with full evidence trail.
Action requires operator confirmation. Held until a human approves. Only then does it execute. Operator stays in control.
Action blocked. Command never reaches the target system. "I don't know" is always safer than guessing. Fail-closed by default.
Live proof — OT/ICS demo
MIG was tested against a simulated oil processing facility running on LabShock's Oilsprings Air environment. Real OpenPLC. Real SCADA. Real Modbus protocol. These are actual MIG decisions.
6 commands attempted. 0 unsafe executions. 2 operator-approved changes executed safely. Pump speed changed on SCADA dashboard only after operator confirmation. Oldsmar-style attack (9500% setpoint deviation) blocked instantly. Plant integrity maintained throughout.
How MIG is different
Most OT security tools monitor network traffic and alert after something happens. MIG validates the action before it reaches the control system. Different category.
| Capability | Network Monitors (Claroty, Dragos, Nozomi) | MIG |
|---|---|---|
| When it acts | After execution — detects and alerts | Before execution — validates and decides |
| AI agent governance | Not designed for agentic AI | Built specifically for AI agent actions |
| Payload inspection | Network packet analysis | Semantic intent + data type + destination analysis |
| Default on unknown | Alert (fails open) | Deny (fails closed) |
| Operator approval | Manual investigation after alert | Built-in approval workflow before execution |
| Audit trail | Network logs | Full decision chain — command, policy, checks, flags, risk score |
| Domain | OT network security | Any system where AI agents execute actions |
MIG is not a replacement for network monitoring. It's a layer that doesn't exist yet — pre-execution AI governance. These tools are complementary, not competitive.
The founder
I started building MIG because I saw a gap that nobody was addressing: AI agents are making real-world decisions, and there's no external system checking whether those decisions are safe before they execute.
Every AI safety tool I found was either monitoring after the fact, or trying to control what the model thinks. Neither approach solves the execution problem. An agent can be compromised, hallucinate, or simply misunderstand — and if nothing validates the action before it happens, the damage is done.
So I built MIG as the execution boundary. It doesn't care what the agent thinks. It inspects what the agent is actually trying to do — what data it's carrying, where it's sending it, whether the command matches policy — and decides ALLOW, DENY, or APPROVAL before anything touches the real world.
I've been building this solo for two years. No team, no funding. The product works — it's been validated on a live PLC controlling pump speeds in a simulated oil plant, and it's running HR governance policies for a pilot partner. The engine is the same for both. Different policies, same execution boundary.
I'm looking for the right partners — investors who understand infrastructure, and early customers who need AI governance that's structural, not optional.
Get in touch
We work with organizations deploying AI agents in critical and regulated environments. If your AI systems take real-world actions, we should talk.
neel@houseofgalatine.com