MOUNTAIN THEORY VS NOMA SECURITY
Noma is the highest-funded independent AI security company and leads on data context and posture. Mountain Theory is runtime-first. The two answer different questions: what is my exposure, versus what happens the moment an agent acts.
| Noma Security | Mountain Theory | |
|---|---|---|
| What it controls | AI assets, data exposure and posture | The action an AI agent is about to take |
| Where it sits | Alongside, mapping and alerting | Inline at execution, between the decision and the action |
| How policy is set | Risk policy and posture rules | Plain English, no code |
| Deployment reach | Broad platform coverage, data-centric | Model and framework agnostic, including custom and on-prem agents |
| Best fit when | You need inventory and data-risk visibility first | An AI acting wrongly has physical or regulatory consequences |
Why you might pick Noma Security
Noma has the best data context in the market, distinguishing PII from public data at the source, and the resources to outspend almost anyone on enterprise sales. Fortune 500 logos lower buyer risk, the founders came out of Unit 8200, and Gartner recognises them in AI TRiSM. Their agentic risk map is genuinely good at visualising lateral movement.
Why you might pick Mountain Theory
Noma is posture-heavy and runtime is an addition rather than the core. Mountain Theory is runtime-first: the risk map is not the point, the immediate transaction is. Noma visualises and alerts. Mountain Theory intercepts and blocks before the action runs.
The honest verdict
Noma will show you a beautiful map of your agentic risk. At 02:00 on a Sunday, when an agent reads a poisoned support ticket and starts exfiltrating a customer table, the map updates and an alert fires. Nothing stops the query. Mountain Theory checks that query against policy before it reaches the database, so the incident becomes a blocked action in a log rather than a breach notification.
What we can actually show
Claims in this category are easy to make and hard to check, so here is ours on the record. The same 10 actions were run in the same order under three configurations. Ungoverned, 10 of 10 executed. Under NVIDIA OpenShell alone, all 5 sandbox-boundary crossings were denied at the kernel, and all 3 in-bounds bad decisions still went through, including a secrets read that printed credentials to the screen. Under OpenShell plus Mountain Theory, those same 3 actions returned HOLD, HOLD and BLOCK, and the secrets read was stopped before it executed, so the credentials never printed. Terminal recordings of all three runs are published, including the two actions Mountain Theory has no policy for.
Separately, when a third-party provider updated the foundation model driving an autonomous agent, the agent began attempting multi-step actions it had never tried before. Nothing on our side changed. Every attempt was stopped on 30 and 31 July 2026, the days the behaviour first appeared. No new rule, no signature, no patch.
Watch the three-configuration run against NVIDIA OpenShell
See novel agent behaviour stopped the day it appeared
Ask Noma Security, and every other vendor you are evaluating, for the same four things: the exact action set, the ungoverned control condition, the outcome per action including the ones the product did not stop, and the recording. A certification, an integration list or a customer logo answers a different question.
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