MoorAI vs Certiv
Last updated
Same starting move, one decisive divergence. Certiv (certiv.ai) calls itself “AI Agent Assurance for Endpoints” (certiv.ai) — it installs on the workstation, discovers AI agents, and continuously authorizes model requests and tool calls before risky actions happen. That is the same architectural stance as MoorAI: on the endpoint, pre-execution, per-action. The overlap is real.
The divergence is what each one does with content. Certiv's edge is an intent engine that “evaluates purpose, not just pattern” (certiv.ai), and to reason about purpose it reads the agent's context. Certiv's own material lists what that visibility covers: user prompts, model reasoning steps, tool calls and data. That is content-aware by design, and it is what buys Certiv its breadth. MoorAI takes the opposite stance: it is content-free by architecture. Prompt and response content never leaves the device; only category · risk · keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival) do. And because MoorAI's agent is open source (MIT), that content-free property is auditable — you can read the interceptor and the exact signal schema — rather than asserted by a closed binary.
Each side gives something up. Certiv reads more to understand more, across more platforms. MoorAI sees less on purpose, and proves it in the open.
Where MoorAI goes deeper
Content-free evidence, and the auditability that makes it trustworthy. A content-aware intent engine has to read prompts and reasoning to do its job; MoorAI decides on the device and emits only a redacted signal. Because it is open source, the guarantee is checkable rather than asserted. Certiv publishes no source code, so several of these rows are marked — unconfirmed, not absent: closed material does not let us verify them either way.
Scroll sideways →
| MoorAI | Certiv | |
|---|---|---|
| Content-free by architecture — prompt/response content never leaves the device | ✓content-free by construction | ✗intent engine reads prompts + reasoning (their own material) |
| Only category · risk · keyed one-way hash (plus login and hostname, pseudonymised on arrival) leave the device | ✓redacted signals only, HMAC-SHA-256 | —telemetry contents not published |
| Open source (MIT) — the content-free claim is auditable | ✓MIT, readable interceptor | —no licensed source found |
| Reviews AI output, not just the prompt and the action | ✓reviews prompts + responses | —action-focused; response review unconfirmed |
| Per-tool MCP argument rules | ✓per-tool arg inspection | —tool-call governance; arg-level rules unconfirmed |
| Model-endpoint allow-list (Agency Enforcement) | ✓approved-endpoint allow-list | —authorizes model requests; endpoint allow-list unconfirmed |
| Cryptographically signed, tamper-evident decisions | ✓signed per decision | —unconfirmed |
| Compliance-evidence packs mapped from content-free signals (EU AI Act / NIST AI RMF / ISO 42001) | ✓control mapping from redacted signals | ◐governance reporting; content-free control mapping unconfirmed |
| Content-free data lineage / event flow (source → actor → destination) | ✓content-free lineage view | —visibility present; content-free lineage unconfirmed |
| Rules-file / skill-file poisoning detection (fingerprint only) | ✓fingerprint only, content-free | —unconfirmed |
| Named lethal-trifecta / cross-server toxic-flow detector | ✓named content-free detector | ◐frames exfil + supply-chain threats; named detector unconfirmed |
| Content-free per-agent assurance score | ✓per-agent, content-free | —“assurance” positioning; per-agent score unconfirmed |
| On-device OCR of image inputs (macOS + Windows) | ✓macOS Vision + Windows OCR first-class; Linux Tesseract opportunistic | —unconfirmed |
| On-device model escalation on ambiguity — opt-in, default off | ◐regex-first; opt-in on-device model second opinion, content-free | —unconfirmed |
| Natural-language policy authoring | ✓plain-language rules | ◐intent-based policy; NL authoring unconfirmed |
| Break-glass / offline fail-closed | ✓offline fail-closed | ◐keeps enforcing off-network |
| Runs with no account and no console to stand up | ✓open agent, no account required | ✗managed platform |
| Free & open source (console free self-hosted, no user limit) | ✓MIT; console free self-hosted | ✗commercial product |
Where we overlap
Certiv's architecture is close to MoorAI's, and the table marks it that way. Both live on the endpoint, both authorize each action before it runs, both govern MCP tool calls, both discover shadow agents, and both map their threat model to OWASP and MITRE.
Scroll sideways →
| MoorAI | Certiv | |
|---|---|---|
| Detection and enforcement both run on the endpoint | ✓ | ✓ |
| Pre-execution enforcement — stops the action before it runs | ✓ | ✓ |
| Per-action / continuous authorization, not session-based | ✓ | ✓ |
| On-device MCP tool-call governance | ✓ | ✓ |
| Intercepts tool calls / API requests before execution | ✓ | ✓ |
| Shadow-AI agent discovery across endpoints | ✓ | ✓ |
| Names the coding agents explicitly (Claude Code, Codex) | ✓ | ✓ |
| Runtime host / process / network context at decision time | ✓ | ✓ |
| Works with hosted and local models | ✓ | ✓ |
| OWASP / MITRE threat mapping (exfil, credential theft, RCE, privilege escalation, supply chain) | ✓ | ✓ |
| Container + CI/CD coverage | ✓server mode: CI runs, the Claude Code GitHub Action, Agent SDK containers | ✓containers + CI/CD |
Where Certiv covers more ground
Certiv leads here. Being content-aware buys Certiv reach that MoorAI does not attempt. It runs on more platforms, governs every AI agent on the endpoint rather than just the coding ones, models intent semantically, and ships as a managed enterprise platform. If the requirement is “govern the whole agentic workforce across a mixed fleet,” that is Certiv's product and not ours.
Scroll sideways →
| MoorAI | Certiv | |
|---|---|---|
| Linux endpoint agent (first-class parity) | ◐the agent ships for Linux (.deb or tarball); no Linux desktop app | ✓Mac / Windows / Linux |
| Governs the whole agentic workforce — any AI agent, not just coding agents | ◐coding-agent focused | ✓any agent on the endpoint |
| Semantic intent modeling — “evaluates purpose, not just pattern” | ◐category/risk classification, not full intent model | ✓intent engine |
| Model reasoning-step / reasoning-chain visibility | ◐prompt + response, not full reasoning chain | ✓processes model reasoning chains |
| Agent-to-agent interaction as a first-class monitored dimension | ◐cross-server toxic-flow detection, narrower | ✓tracks agent-to-agent interactions |
| SaaS-application action governance | ◐content-free browser extension across 8 GenAI apps, narrower than all-SaaS | ✓monitors SaaS applications |
| Interactive pause / hold-for-approval workflow at the moment of action | ◐JIT elevation, adjacent | ✓block / pause / approve |
| Vendor SOC 2 certification (managed platform) | ✗self-hosted open source — you audit the code instead | ✓AICPA SOC 2 |
Where MoorAI is stronger. Content-free evidence you can audit. In a category whose whole promise is “we govern your agent without hoarding your prompts,” the strongest version of that promise is one you can check. MoorAI is content-free by architecture (only category · risk · keyed one-way hash, plus the login and hostname, which the console pseudonymises on arrival, leave the device), and it is MIT, so the guarantee is readable rather than asserted. Three exceptions: an administrator can turn on a capture tier that does store the matched text it was enabled to collect; on a platform whose OS cannot read an image locally the desktop app can send a pasted screenshot to Anthropic’s API for text extraction, using an Anthropic API key already on the device and never us; and an opt-in model second opinion can send the text of an ambiguous scan to Anthropic’s API the same way. On the coding-agent surface it also goes deeper: detectors mapped to both the OWASP LLM Top 10 and the OWASP Agentic Top 10 (ASI01–ASI10), a CycloneDX/SPDX AIBOM with in-toto/SLSA attestation of the action chain, a content-free data-lineage view, a named lethal-trifecta detector, per-tool MCP argument rules, and a content-free per-agent assurance score that is trustworthy precisely because you can read the code that computes it.
Where Certiv is stronger. Breadth, and a managed platform. Reading the agent's context lets Certiv model intent rather than category, and it spends that on reach: Mac, Windows, Linux and containers; the whole agentic workforce rather than just coding agents; model-reasoning-chain and agent-to-agent visibility; SaaS-application governance; interactive pause-and-approve workflows; and a SOC 2-certified managed platform for teams that want to buy an outcome rather than run an agent. MoorAI’s agent runs on macOS, Windows and Linux, and in CI runs and containers through server mode, with its desktop app on macOS and Windows only, and is deliberately narrow. Those are different purchases for different problems.
Using both. They are more complementary than opposed. Run Certiv to govern the whole agentic workforce across a mixed fleet with a managed, intent-aware platform. Run MoorAI where developers run coding agents with terminal, filesystem and MCP access, where you need the tool call governed at call time, and where the evidence an auditor reads must contain no one’s prompt content at all — produced by an agent whose source you can read.
Certiv capabilities are mapped from Certiv’s own published product and approach pages (certiv.ai) as of 2026; quoted phrases are theirs. ◐ = partial — present but narrower than the other column. — = unconfirmed, not necessarily absent: it marks a capability Certiv’s published material does not clearly document, and because Certiv publishes no source code it cannot be verified either way. MoorAI marks reflect shipped capability. Certiv 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.