MoorAI reviews prompts on the device — nothing leaves it. Detection and enforcement run locally on the endpoint, in front of the AI agents your developers run: Claude Code, Codex, GitHub Copilot CLI. Content never leaves the machine; security teams see only redacted, content-free signals. Open source (AGPL-3.0).
Harmonic Security is zero-touch, point-of-use GenAI DLP — a browser extension plus an endpoint agent, backed by fine-tuned small language models that coach, block, or monitor. It covers a wide GenAI surface: Claude Desktop, ChatGPT Desktop, Cursor, Copilot, Ollama, CLI tools, and 1,000+ AI web apps. This is an honest, architecture-level comparison of two point-of-use tools that draw the trust boundary in different places.
Both tools sit at the point of use, so the real difference is where classification runs. Harmonic intercepts content before it reaches the third-party AI, then classifies it — and that classification runs in Harmonic's own AWS cloud, so content leaves the endpoint to be inspected. MoorAI's check runs entirely on the device: the prompt is evaluated locally and the content itself never leaves the machine. For teams whose hard requirement is “prompt content must not leave the laptop, to anyone,” that is the clean line between the two.
| MoorAI | Harmonic Security | |
|---|---|---|
| Where content is classified | On the device | In Harmonic's cloud (AWS) |
| Prompt content leaves the endpoint | Never | Yes — to be classified* |
| Point-of-use enforcement | Coach · alert · block · justify | Coach · block · monitor |
| Coding-agent action depth | Destructive-cmd block · sign-off gates · output interception | DLP-focused across surfaces |
| Browser / web-app coverage | Endpoint agents (CLI-first) | 1,000+ AI web surfaces |
| Blocks secrets read into agent context | ✓ Claude Code hooks · Codex / Copilot CLI detect-only | Not stated for coding agents |
| MCP tool-call interception (arguments) | ✓ Inspects mcp__* call arguments | — |
| MCP allow-list enforced at call time | ✓ Non-allow-listed servers blocked | — |
| Reviews AI output, not just prompts | ✓ Output-stage detectors + CLI redaction | DLP-focused on data going in |
| Runs fully on-device (no egress) | ✓ Always | Classification runs in Harmonic's cloud* |
| Content-free telemetry | ✓ Category, risk level, one-way hash | Content classified in-service |
| AIBOM export (AI bill of materials) | ✓ HTML / JSON / CSV, CycloneDX-loose | Shadow-AI app discovery |
| Classification engine | On-device rules & content matrix | Fine-tuned SLMs, published accuracy |
| Enterprise GTM / MDM maturity | Emerging | Mature |
| Licensing | Open core (AGPL-3.0 agent) | Proprietary |
| Account required to start | No (community agent) | Enterprise |
Where Harmonic is stronger. Harmonic has mature browser coverage across 1,000+ AI web surfaces, an enterprise go-to-market with MDM-grade deployment, and published accuracy claims for its fine-tuned small language models. For broad, zero-touch GenAI DLP across every web tool an organization touches, that breadth is real.
Where MoorAI is stronger. No-egress: the check runs on the device and prompt content never leaves it. MoorAI also goes deeper on coding-agent actions — blocking destructive commands, gating sign-off-worthy operations, and intercepting agent output, not just screening data going in — and it's open core.
Context interception sharpens that contrast. MoorAI now guards what an agent reads, not only what a person types — an agent pulling a .env into its context, or passing a secret as an MCP tool-call argument, is caught at the read. That check happens entirely on the device. The equivalent under a cloud-classification model would mean shipping the contents of the developer's local files off the endpoint to be classified — which is precisely the architectural difference footnoted below, and it matters more once the guarded surface includes local file reads rather than just typed prompts.
*Harmonic “intercepts before exposure” — that is, before content reaches the third-party AI — but its classification runs in Harmonic's own AWS environment, so content leaves the endpoint to be inspected. This is stated as a factual architectural difference, drawn from Harmonic's published material. Note: Harmonic Security (point-of-use GenAI DLP, backed by Ten Eleven and Next47, ~$26M raised) is a distinct company from the math-reasoning lab “Harmonic AI” — the two are unrelated and should not be conflated. Harmonic Security is a trademark of its owner; this page is not affiliated with or endorsed by it. Comparison is architecture-level — verify current specifics against each vendor's docs. A “—” cell means we could not confirm the capability either way from published material; it is not a claim that the capability is absent.
Recently shipped in MoorAI — all on-device and content-free: a lethal-trifecta detector, rules-file poisoning detection (CLAUDE.md / .cursorrules), a capture-tier toggle (content-free by default), per-tool MCP argument rules, a per-agent assurance score, a data-lineage / Event Flow view, on-device model escalation, and cryptographically signed agency decisions — mapped across OWASP LLM Top 10, NIST CSF, NIST AI RMF, SOC 2, ISO 27001/42001, and the EU AI Act.