This is the closest architectural comparison we draw, so it starts with what Noma gets right. Noma Security is a well-funded enterprise AI-security platform — $132M raised, including a $100M Series B in 2025 led by Evolution Equity Partners — and it ships a real coding-agent product. Noma publishes open-source, Apache-2.0 guardrails plugins that hook the same coding agents MoorAI wraps: Claude Code, Codex, Cursor and GitHub Copilot. In Noma’s own words the plugins “hook into each agent framework’s native extension points” and cover shell execution, MCP tool calls, file reads and prompt submission (github.com/Noma-Security/noma-marketplace). So this is not a different layer. It is the same layer, and the honest comparison is about the data model underneath the hook.
The divergence is where evaluation runs and what leaves the machine. The open-source part of Noma is the thin hook client. By its own documentation, the plugin “stream[s] hook events to the Noma backend for policy evaluation, returning enforcement decisions to the agent”, evaluated by “the Noma AIDR platform… with full visibility in the Noma Console”, and it requires a Noma API key (github.com/Noma-Security/noma-marketplace). The plugin masks some secret-looking values locally, but the detection itself is a cloud console: an independent 2026 review describes Noma as a “SaaS-delivered governance control plane” whose telemetry routes to external infrastructure (predictionguard.com). MoorAI evaluates on the developer’s own machine. By default only category · risk · keyed one-way hash (HMAC-SHA-256) leave the device — the prompt, the command and the file content do not — and because the detector is open source (AGPL-3.0), that guarantee is auditable rather than taken on the vendor’s word.
And the sharpest divergence is measurement. As of 2026 we could find no published detection accuracy, recall, precision or third-party benchmark from Noma — its material describes capabilities and customer outcomes, not an efficacy number. MoorAI’s whole wedge is the opposite: it publishes its marginal value — what it catches that the deployed assistant would not have refused on its own — against a public Apache-2.0 benchmark graded to AMTSO’s product-testing guidelines on a locked held-out split. On the current cut, the deployed assistant stops 36 of 44 attacks (81.8%) unaided; of the 8 it misses, MoorAI catches 7, and 1 gets past both layers. Those runs are self-run by the maintainer, who also authors MoorAI — stated in the results, not hidden.
Same hook, different underneath: on the device, content-free, with a detector you can read and a published number for what it actually adds. Several Noma rows are marked — unconfirmed, not absent, because Noma’s backend is proprietary and its public material does not document these either way.
| MoorAI | Noma Security | |
|---|---|---|
| Detection runs on the device, not in a vendor cloud | ✓on-device evaluation | ✗hook events streamed to the Noma backend for evaluation (their docs) |
| Content-free by construction — prompt / command / file content never leaves the device | ✓content-free by architecture | ✗events sent to backend; local masking only |
| Only category · risk · keyed one-way hash leave the device | ✓redacted signals only, HMAC-SHA-256 | ✗inspects + evaluates events server-side |
| The detector itself is open source — the content-free claim is checkable | ✓AGPL-3.0 detector, readable interceptor | ◐plugin Apache-2.0, but the evaluating backend is proprietary |
| Published marginal value — what it catches beyond the model’s own refusals | ✓7 of the 8 the model misses, on a locked held-out split | ✗no published efficacy number found |
| Runs standalone with no account, no API key, no console to stand up | ✓agent runs offline, free to 200 users | ✗plugin requires a Noma API key + Console |
| Works fully offline / air-gapped with zero external egress | ✓offline fail-closed default, no vendor cloud | ✗review notes no zero-egress / air-gapped mode (predictionguard.com) |
| Blocks a secret being read into context, not just flagged for the console | ✓per-tool arg rule at read time, on-device | ◐protects file reads, but the decision is computed in the cloud |
| On-device OCR of image inputs (macOS Vision + Windows OCR) | ✓on-device OCR | —unconfirmed |
| Content-free browser GenAI prompt interception (on-device extension) | ✓on-device extension, 8 AI chat apps | —unconfirmed |
| On-device semantic second opinion — content-free, opt-in, no vendor-cloud egress | ✓opt-in, loopback / own-key model, content-free | ✗cloud AIDR evaluation by design |
| Cryptographically signed, tamper-evident decisions carrying in-toto / SLSA attestation | ✓signed per decision | —unconfirmed |
Noma is not marked absent here. It ships open-source hooks for the same coding agents, and at the coding-agent surface the two products meet directly — anyone evaluating both should expect a substantial overlap. This is the honest half that a lesser comparison page would hide.
| MoorAI | Noma Security | |
|---|---|---|
| Native hooks in front of Claude Code, Codex, Cursor and GitHub Copilot | ✓ | ✓ |
| Pre-execution review of shell commands and tool calls (before / after) | ✓ | ✓ |
| MCP tool-call mediation and server access controls | ✓ | ✓ |
| Allow / block / ask on risky agent actions (injection, data leak, shell exec) | ✓ | ✓ |
| Sensitive-data (PII / PCI / secrets) detection on the coding-agent path | ✓ | ✓ |
| Cross-platform: macOS, Windows and Linux | ✓ | ✓ |
| MDM / managed-settings fleet rollout (Intune, Jamf) | ✓ | ✓ |
| OWASP LLM Top 10 / MITRE ATLAS threat framing | ✓ | ✓ |
This is the honest half of the page, and it is where Noma’s real weight sits. Noma is a funded enterprise platform, and the coding-agent hooks are one product in a suite. If the requirement is a central governance plane across all of an organisation’s AI — not just the coding agents on a laptop — that is Noma’s product, and MoorAI does not attempt it.
| MoorAI | Noma Security | |
|---|---|---|
| Central enterprise console across the whole AI estate (LLM apps, RAG, agents) | ✗coding-agent endpoint scope only | ✓unified enterprise platform |
| AI-SPM — AI asset discovery & posture management | ✗not attempted | ✓AI-SPM product |
| Fleet-wide discovery of coding assistants & MCP servers via EDR integration | ◐on-device discovery, not fleet-EDR | ✓CrowdStrike EDR integration |
| MCP server supply-chain scanning (unpinned versions, excess permissions, CVEs) | ◐MCP mediation, not a supply-chain scanner | ✓scans approved MCP servers |
| Continuous AI red teaming of AI apps & agents | ◐a local policy self-test (moorai-redteam), not a service | ✓AI Red Teaming product |
| Runtime detection & response across the AI estate (AI-DR) | ✗endpoint coding-agent only | ✓AI-DR product |
| Protects the AI applications you host, not just coding agents you run | ✗coding-agent scope only | ✓LLM / RAG / agent app coverage |
| Enterprise compliance attestations (ISO 27001, SOC 2, HIPAA) | ✗open-source project, no vendor attestations | ✓ISO 27001 · SOC 2 · HIPAA (their site) |
| Funded vendor with enterprise support & account management | ✗community open source | ✓$132M raised, commercial support |
Where MoorAI is stronger. Content-free evidence you can audit, at the coding agent. Both products hook the same agents, so the difference is what happens after the hook fires. Noma’s plugin sends the event to its cloud to be judged; MoorAI judges it on the machine and, by default, only category · risk · keyed one-way hash leave. Because the detector is AGPL-3.0, the content-free claim is one you read rather than one you trust — and the two exceptions are named in the open: an administrator can turn on a capture tier that stores the matched text it was enabled to collect, and on a platform whose OS cannot read an image locally a pasted screenshot goes to your own AI provider for text extraction, never to us. It also carries the measurement Noma does not publish: a marginal-value figure — 7 of the 8 attacks the deployed assistant misses — on a locked held-out split, graded to AMTSO’s guidelines, with the false-positive line printed beside it.
Where Noma Security is stronger. The enterprise plane across all of your AI. Noma is a funded platform whose coding-agent hooks are one module beside AI-SPM, Agent Access Control, AI Red Teaming and AI-DR — discovery across a fleet through EDR, MCP supply-chain scanning, posture management, runtime detection and response, and coverage for the LLM applications, RAG systems and agents an organisation hosts, not just the coding agents developers run. It rolls out centrally through managed settings and MDM, carries ISO 27001, SOC 2 and HIPAA, and comes with the support and account management of a $132M-backed vendor. MoorAI does none of the estate-wide work; it ships a single, focused, on-device tier for the coding agent.
Choosing between them. They overlap more than most pairs on this site, so the choice is real. Pick Noma if you need a central enterprise console governing every kind of AI across the organisation, fleet discovery, red teaming and runtime response, and you are comfortable that coding-agent events are evaluated in the vendor’s cloud. Pick MoorAI where the requirement is that the coding agent is governed on the developer’s own machine, the evidence an auditor reads contains no one’s prompt content at all, the tool runs offline with no account, and the detector — and the number for what it actually adds — are open enough to check yourself.
Noma Security capabilities are mapped from Noma’s own published material (noma.security and github.com/Noma-Security/noma-marketplace) as of 2026; quoted phrases are theirs, except the “SaaS-delivered governance control plane” description, which is from an independent 2026 review (predictionguard.com). ◐ = partial — present but narrower or differently sited than the other column. — = unconfirmed, not necessarily absent: it marks a capability Noma’s public material does not clearly document, and because the Noma backend is proprietary it cannot be verified either way. MoorAI marks reflect shipped capability. MoorAI’s figures are self-run by the maintainer, who also authors MoorAI — stated, not hidden — on a locked held-out split of the public Agentic Security Benchmark, graded to AMTSO’s Guidelines for Testing of Agentic Security Products; OPSWAT, where Jan Miller is CTO, is an AMTSO member, but AMTSO has not reviewed, certified or endorsed MoorAI or the benchmark. Noma Security is a trademark of its respective owner; this is an independent capability comparison, not an endorsement or an affiliation, and every product here evolves — verify specifics against their current documentation.