MoorAI vs Sun Security
Last updated
Both check an AI agent’s actions before they run, and both do it from inside the coding agents people use, through those agents’ own hooks. Sun holds each agent to a purpose it derives for that agent; MoorAI decides each tool call and model call against a signed policy by what the action contains and what the user asked for. Sun describes its product as follows: “Sun sits in the path of every AI agent in your environment, keeping each one true to what it was built to do.” (sun.security) Its policy is derived, not written: “Sun learns each agent's purpose automatically, derived from its instructions, tools, the process it serves, and how it is actually used. Derived, not configured.” (sun.security/llms.txt) And it enforces in the path: “Every tool call, every connection, every resource draw is checked against the agent's purpose before it executes.” (sun.security) MoorAI runs where the agent runs. On employee endpoints it is a PreToolUse hook inside the coding agent and an MCP stdio proxy in front of its tool servers. On AI servers (MoorAI for AI Servers, which inspects model calls, checks tool use and enforces policy where the AI runs) it is server mode of its hook in claude -p CI jobs, the Claude Code GitHub Action and Agent SDK services in containers, @moorai/agent-sdk in the service’s own process, the moorai-serve localhost sidecar for other agent loops, an MCP gateway in front of remote MCP servers and a model proxy in front of model APIs. It reads the prompt, the file read into context, the shell command, the Model Context Protocol (MCP) arguments and results and the tool calls in a model’s response, and coaches, masks or blocks on what it finds.
They meet head on at the workstation. Sun: “For workstation agents such as Claude Code, Claude Cowork, and Cursor, Sun integrates into the agentic platform through hooks and sees every action, including MCP usage, Skills, plugins, web actions, files, token usage, and session mode.” (sun.security/llms.txt) MoorAI’s hook is validated end to end on Claude Code, and Codex CLI, GitHub Copilot CLI, Gemini CLI, Cursor and Factory Droid block through their own pre-tool hooks. Where the two differ is the question asked of each action. Sun asks whether it fits what the agent is for. MoorAI asks whether this command, file, argument or result carries a secret, PII, an injected instruction or a destructive operation, and whether the user’s own request named its target. MoorAI decides against a 85-threat matrix on the device and by default sends only category · risk · keyed one-way hash (plus the login and hostname, which the console pseudonymises on arrival).
Sun reaches agent platforms MoorAI does not. Sun lists “SaaS agent platforms (Copilot Studio, n8n, Agentforce), cloud environments (Amazon Bedrock, Azure AI Foundry, Google Vertex AI), LLM gateways (LiteLLM, Azure APIM), and device-based agents (Claude Code, Claude Cowork, Cursor)”. (sun.security/llms.txt) MoorAI does not cover the SaaS agent platforms or Azure AI Foundry and Vertex AI, and its Amazon Bedrock support is a read-only inventory. The rows where Sun covers more ground are further down.
Three things this page does not state. (1) What Sun’s hooks send to Sun, and what Sun keeps. Sun’s terms describe “Sun Security's hosted security platform for AI agents, automation agents and AI-enabled workflows” (sun.security/terms-and-conditions), and its privacy policy says “customers may submit, upload, transmit or otherwise make available data, logs and other content for analysis and monitoring” (sun.security/privacy-policy); which fields an action record carries is not described. (2) How Sun stops an action on a SaaS or cloud agent platform. Hooks are named for workstation agents only; the enforcement point for Copilot Studio, Bedrock or a gateway is not described. Sun’s documentation site, docs.sun.security, requires a sign-in and was not read. (3) Pricing. We found no price list on Sun’s site; its pages lead to a demo request. Sun’s public material is short: a home page, two integration pages, a support page, its legal pages and its llms.txt. Every Sun statement below is quoted from Sun’s own site, and the page is named.
The core difference is what an action is measured against: what the agent is for, or what the action contains and what the user asked for.
.env pasted into an MCP call the agent makes every day, a destructive command on a path the prompt never named, an injected instruction in a fetched page. That catches harm inside the agent’s normal job, and it needs a rule for each kind of harm. MoorAI’s nearest equivalents to Sun’s purpose are declared, not derived: a workload profile of the tools, MCP servers and hosts a service is expected to use, an entitlement envelope of authorised tools, path prefixes and MCP servers per agent, and task-bound tool calls, which hold a high-impact call to the person’s own task.Where MoorAI holds ground Sun does not
These rows follow from reading the content of each action on the device, before it runs, and from keeping that content off the console.
Scroll sideways →
| MoorAI | Sun | |
|---|---|---|
| Coding agents beyond Claude Code and Cursor: Codex CLI, Copilot CLI, Gemini CLI, Factory Droid | ✓pre-tool hooks; validated end to end on Claude Code | —names Claude Code, Claude Cowork and Cursor |
| Blocks a secret at the file read, before it enters the model’s context | ✓hooks the agent's file-reading tools | —not in public material |
| Catches a harmful action that fits the agent’s normal job, by its content | ✓on-device threat matrix | —decides on fit to the agent's purpose |
| Detects an injected instruction in what the agent reads | ✓detectors on ingested content; session taint | —catches the off-course action, not described as reading content |
| Masks a secret or PII span and lets the call proceed | ✓mask action | — |
| Coaching with the safer alternative, shown to the developer and the agent | ✓ | — |
| Flags a risky action aimed at something the user’s request never named | ✓intent alignment; lexical, keyed hashes | ◐judged against the agent's purpose, not the user's request |
| By default only category · risk · keyed one-way hash (plus login and hostname, pseudonymised on arrival) leave the device | ✓capture tiers are opt-in, admin-enabled | —hosted; fields sent not public |
| Open source, so the data claim can be checked in code | ✓agent MIT | ✗proprietary |
| Public product documentation, no demo call | ✓ | ✗docs.sun.security requires a sign-in |
| Free to start and to enforce, without talking to sales | ✓agent free; console free self-hosted, no user limit | ✗no public pricing; demo-led |
Where Sun covers ground MoorAI does not
This list is shorter, and it matters. Sun reaches the agents a company builds on SaaS and cloud platforms, and it derives each agent’s policy instead of asking anyone to write it.
Scroll sideways →
| MoorAI | Sun | |
|---|---|---|
| Agents on SaaS agent platforms: Copilot Studio, n8n, Agentforce | ✗ | ✓enforcement point not public |
| Cloud agent platforms: Amazon Bedrock, Azure AI Foundry, Google Vertex AI | ◐Bedrock inventory only, read-only, not yet run against a real AWS account | ✓ |
| Third-party LLM gateways: LiteLLM, Azure APIM | ✗ships its own model proxy and MCP gateway instead | ✓ |
| Each agent’s policy derived automatically from its instructions, tools and use | ◐declared profiles and envelopes; observed destination map; nothing drafts the profile | ✓“Derived, not configured.” |
| Claude Cowork actions through a workstation hook | ◐Inference hooks beta server only; tested on sample frames | ✓ |
| Security alerts routed to Slack channels by alert type and severity | ◐alert email and SIEM push; Slack or Teams webhook for approval requests | ✓ |
| Deletes flagged Claude Enterprise conversations by response policy | ✗read-only by design | ✓Compliance API delete scope |
Same capability, different mechanism
Both products do each of these, so the table describes how rather than scoring.
Scroll sideways →
| MoorAI does it by… | Sun does it by… | |
|---|---|---|
| Stopping an agent’s action before it takes effect | A PreToolUse hook in the agent’s own process, validated end to end on Claude Code. Codex CLI, Copilot CLI, Gemini CLI, Cursor and Factory Droid block through their own pre-tool hooks. An MCP stdio proxy enforces for any host that launches a stdio MCP server. A device that is not enrolled in a MoorAI console coaches and never blocks. | Hooks in workstation agents, and a check in the path: “enforced deterministically in the path, before actions execute”. (sun.security/llms.txt) |
| Finding the agents | An on-device AI bill of materials (agent hosts, MCP servers, IDE extensions, local model servers and AI keys at rest, exportable as CycloneDX or SPDX), Skill Analysis of the skills, sub-agents, commands and MCP configs an agent auto-loads, coverage integrity findings for an agent in use with no MoorAI hook traffic, and a read-only Amazon Bedrock inventory. | “Sun finds all agentic tools across your organization, then the agents themselves across all surfaces” (sun.security), and “Sun maintains a live, non-intrusive inventory of AI agents, MCPs (local and remote), Skills, and Extensions across the stack, and maps each agent's exposure.” (sun.security/llms.txt) |
| Token usage | Content-free hourly totals per agent, model and keyed project, from the model proxy and from Claude Code’s own transcripts, with signed usage budgets the model proxy enforces by answering 429. | “Sun maps token usage by purpose, showing what AI agents are actually being used for and what each purpose costs.” (sun.security/llms.txt) |
| Claude Enterprise through Anthropic’s Compliance API | A read-only console integration: the Activity Feed stored content-free, alerts for Claude admin and configuration changes, people active in Claude whose devices show no MoorAI coverage, and the organization’s effective settings next to MoorAI’s policy. Tested against a fake server built from Anthropic’s documentation. | Conversation monitoring, sensitive-data detection and deletion; its setup asks to “Create a Compliance Access Key with user, org, activities, and delete scopes.” (sun.security/integrations/claude-enterprise) |
| Alerts in chat | Alert email and a SIEM push; approval requests go to an approver group by email and, when set, a Slack or Teams incoming webhook, content-free, with device and user pseudonyms. | “Sun delivers its AI security alerts into a Slack channel you choose, showing the severity, host, user, and the reason each alert fired.” and “The integration posts messages only and never reads your conversations.” (sun.security/integrations/slack) |
| Asking a person to stand behind an action | A justify verdict requires a signed justification; just-in-time approval routing sends selected blocks and asks in Claude Code to an approver group, and the device allows the identical call only after an approve grant arrives inside the signed policy. | Its privacy policy says “we deliver security alerts and justification notifications into a Slack channel that you select”. (sun.security/privacy-policy) The justification flow itself is not described. |
MITRE ATLAS, as documented. In our reading of vendor material against the 76 ATLAS techniques tagged Agentic AI, Sun’s published material documents 2 of the 76: AML.T0053 AI Agent Tool Invocation and AML.T0103 Deploy AI Agent, each with a limit on what it covers (read 29 October 2026). The home page’s list of risks (manipulated behaviour, unvetted tools and MCP servers, agents that drift from their goals) describes problems rather than mechanisms, its scrolling list of incidents illustrates harm rather than claiming Sun caught it, and the chain-of-actions claim rests on the same purpose check already counted, so none of them adds a cell. That counts what the public pages say; Sun’s documentation is behind a sign-in and was not read. The method and every quote behind the number are in MITRE ATLAS agentic-technique coverage.
Where MoorAI is stronger. What is inside each action, decided before it runs, on the device. The file read, the shell command, the MCP argument and result, the model’s tool call, the prompt and the output are read where the agent runs and coached, masked or blocked there, including when the harmful action fits the agent’s job: a secret in an approved MCP call, an injected instruction in a fetched page, a deletion on a path the user never named. It reaches more coding agents by name, and the same policy covers agent services through server mode, the Agent SDK, the moorai-serve sidecar, the MCP gateway and the model proxy. The console receives no content, the code that guarantees that is MIT, and enforcing is free on a self-hosted console.
Where Sun is stronger. Breadth of agent platforms, and a policy nobody has to write. Sun covers agents built on SaaS platforms, cloud AI platforms and LLM gateways as well as workstation agents, in one product, and derives each agent’s purpose from its instructions, tools and use, so a deviation is caught without an administrator declaring a profile first. It ties token spend to what each agent is for, routes alerts into Slack by type and severity, and can act on Claude Enterprise conversations through the Compliance API. It also sells a short start: “Get started today in just 5 minutes.” (sun.security) For a company whose agents live in Copilot Studio, n8n, Agentforce or a cloud agent platform, Sun covers ground MoorAI does not.
Which to run where. On coding agents the two overlap directly and the choice is one of method: Sun if you want each agent held to a derived purpose with no policy to write, MoorAI if you want each action judged by its content and by the person’s request, with a record that holds no prompt content and code you can read. On SaaS and cloud agent platforms, Sun reaches agents MoorAI does not. On agent services that call model APIs and MCP servers, MoorAI’s server mode, SDK, sidecar, gateway and proxy apply the same signed policy. Nothing in either’s public material says they conflict, and we have not run them together.
Questions about MoorAI and Sun Security
Do Sun Security and MoorAI both work inside coding agents like Claude Code and Cursor?
Yes. Sun says that for workstation agents such as Claude Code, Claude Cowork and Cursor it integrates into the agentic platform through hooks and sees every action, including MCP usage, skills, plugins, web actions, files, token usage and session mode. MoorAI runs as a pre-tool hook in Claude Code, validated end to end there, and in Codex CLI, GitHub Copilot CLI, Gemini CLI, Cursor and Factory Droid through their own pre-tool hooks, plus an MCP stdio proxy for any host that launches a stdio MCP server. Sun names Claude Cowork; MoorAI reaches Cowork only through its server for Anthropic’s Inference hooks beta, which has been tested against signed sample frames, not the live service.
How does each decide whether an agent action is allowed?
Sun derives each agent’s purpose from its instructions, tools, the process it serves and how it is actually used, and checks every tool call, connection and resource draw against that purpose before it executes. MoorAI checks each tool call and model call before it runs against a signed policy: the threat matrix and content rules (secrets, PII, injected instructions, destructive operations), the user’s own request (intent alignment and task-bound tool calls), and any workload profile or entitlement envelope an administrator declares. Sun’s question is whether the action fits what the agent is for; MoorAI’s is what the action contains and whether the user’s request named its target.
Which agent platforms does Sun Security cover that MoorAI does not?
Sun lists SaaS agent platforms (Copilot Studio, n8n, Agentforce), cloud environments (Amazon Bedrock, Azure AI Foundry, Google Vertex AI) and LLM gateways (LiteLLM, Azure APIM) alongside device-based agents. MoorAI does not cover Copilot Studio, n8n, Agentforce, Azure AI Foundry or Vertex AI. On Amazon Bedrock it offers a read-only inventory, tested against documented response shapes and not yet against a real AWS account, and in place of a third-party LLM gateway it ships its own model proxy and MCP gateway.
What data does each send to its console?
Sun is a hosted platform, and its privacy policy says customers may make data, logs and other content available to it for analysis and monitoring; which fields its hooks send is not public. Its Claude Enterprise integration uses Anthropic’s Compliance API to monitor conversations and can delete flagged conversations when a response policy says so. MoorAI sends a category, a risk level and a keyed one-way hash by default, plus the login and hostname, which the console pseudonymises on arrival, and keeps content only if an administrator enables a capture tier. Its own Claude Compliance API integration is read-only and stores the Activity Feed content-free.
Is Sun Security’s documentation or pricing public?
No. Sun’s documentation site requires a sign-in, and its pages lead to a demo request with no price list. MoorAI’s agent is MIT and documented in its public repository, and its console is free to self-host with no user limit.
Sun Security capabilities are taken from Sun’s own published material, checked on 29 October 2026: sun.security, its llms.txt, the Claude Enterprise and Slack integration pages, the privacy policy and the terms. Sun’s documentation site requires a sign-in and was not read, and its trust center was not read. Every quoted phrase is Sun’s. Customer quotes on Sun’s pages are not used. ◐ = partial: present but narrower than the other column. — = unconfirmed, not necessarily absent. MoorAI marks reflect shipped capability: hook enforcement is validated end to end on Claude Code; Codex, Copilot CLI, Gemini CLI, Cursor and Factory Droid block through their own pre-tool hooks. The agent is MIT and runs on macOS, Windows and Linux; the console is source-available under the Elastic License 2.0, free self-hosted with no user limit, and paid when hosted by MoorAI. Sun Security is a trademark of its owner; this page is independent and is not affiliated with or endorsed by Sun Security. Both products change, so check specifics against current documentation.