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在 HN 发布:Consus(YC F24)——面向处理敏感数据企业的 LLM 网关

Launch HN: Consus (YC F24) – LLM gateway for companies handling sensitive data

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为涉密行业提供统一合规的多模型AI接入,降低部署与审计门槛。

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Hacker News 的各位好!我是 Consus 的创始人 Eric。我们将所有 AI 请求路由至符合 ITAR 限制和受控非机密信息(CUI)要求的基础设施。Consus 本身符合 ITAR 限制要求,我们最近还完成了针对 410 项 FedRAMP High 控制措施的 FedRAMP 等效性审计。FedRAMP High 是美国政府针对处理敏感非机密数据的云服务制定的最严格安全基线。此类数据泄露可能造成严重或灾难性后果。在处理 CUI 信息之前,必须取得 FedRAMP 等效性或授权。演示:https://youtu.be/6MYxJe6P4oY 航空航天与国防行业正在快速增长。但遗憾的是,由于必要的数据保护措施和合规风险,大多数公司都难以使用 AI。如果这些公司能够使用大多数其他行业习以为常的 AI 工具,它们本可以发展得更快。 在加入 Consus 之前,我的大部分职业生涯都在航空航天与国防领域。我曾在 SpaceX 负责发射国家安全载荷,也是 Castelion 的早期员工。我亲眼看到,合规方面的繁文缛节如何让公司难以采用新技术,也难以信任那些会接触其敏感数据的供应商。 例如,我曾与一家卫星制造商合作,该公司不允许使用任何 AI 工具,即使是处理非敏感工作也不行。尽管过去 9 到 12 个月里 AI 已经变得极其有用,但令我惊讶的是,仍有这么多航空航天与国防公司拒绝接触 AI。他们看得出 AI 可能会有所帮助,但担心模型提供商使用他们的数据进行训练、违反 ITAR 规定,或者因未通过网络安全评估而失去新业务。这些担忧都是合理的。 解决其中部分或全部问题有许多方法,但我们专注于构建一个合规的 AI 推理层,可以“接入”任何 AI 工具或工作流。正如 OpenRouter 能够与大多数工具集成一样,Consus 可以连接到 ChatGPT 或 Claude 这样的工具、包括 Pi coding agent 或 Open WebUI 在内的开源工具,也可以集成到各个组织自行构建和维护的应用程序中。 几乎没有哪家航空航天与国防公司同时使用三大 GovClouds(AWS、Azure、Google)。当我梳理哪些前沿模型可以合法运行于 ITAR 和 CUI 工作场景时,我发现每个提供商都只提供部分模型,而每个模型达到的合规水平也各不相同。 我们的客户不会从云提供商的角度思考。他们考虑的是所需的数据控制措施,以及想要使用的模型。这就是我们构建合规后缀的原因。在底层,每个请求都会访问一个统一的 OpenAI 兼容端点;在接触推理服务之前,系统会先验证密钥并执行策略。每个模型名称都必须带有合规后缀,例如 gpt-5.6-sol:fedramp-high 或 claude-opus-5-5:itar,任何不带后缀的模型名称都会被拒绝。 我们绝不会代理到标准商业端点。客户还可以构建模型允许列表,并为组织内的单个用户分配支出和模型使用限制。所有这些都通过 Consus Portal 处理,团队可以在那里管理支出、密钥和策略。 我们经常听到的一个问题是:为什么不直接在 AWS GovCloud 或 Azure Government 中使用 Bedrock?对于小型团队而言,仅仅获得这些平台的访问权限就很困难。对于大型组织而言,GovCloud 允许你继承部分、但不是全部处理敏感数据所需的控制措施。此外,模型分散在不同提供商之间:GPT-5.6 Sol 仅在 Azure Government 中提供,而 Claude Opus 5.5 则通过 AWS GovCloud 中的 Bedrock 提供。如果两者都想使用,就需要搭建并保护两个独立的政府云环境。 我们将所有模型置于一个端点之后运行,因此客户无需选择云平台,而合规工作则由我们分别为每个平台处理。 鉴于数据的敏感性,Consus 不会在静态状态下存储任何客户数据。所有提示词和生成结果只会在请求处理期间保留于内存中,绝不会记录或写入磁盘。无论是我们自身,还是基础设施及模型提供商,都严格执行零数据保留政策。出于计费和审计目的,我们会保留每个请求和响应的元数据。 Consus 从设计上就不是自助服务产品。ITAR 要求我们确切了解每个密钥另一端的使用者,因此我们会验证每个组织,并在发放 API 密钥前签订合同。 我们向客户收取合规费用,起价为每席位每月 40 美元,另加按成本收取的 AI 令牌费用。我们目前正在积极服务客户,非常期待获得安全专家,以及在高度监管行业工作的开发者和工程师的反馈! 参考资料: API 文档:https://gateway.consus.io/overview 安全与架构:https://gateway.consus.io/security 合规详情:https://consus.io/compliance 评论 URL:https://news.ycombinator.com/item?id=50073471 得分:4 评论数:1

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Launch HN: Consus (YC F24) – LLM gateway for companies handling sensitive data
6 points by ericmags 27 minutes ago | hide | past | favorite | 3 comments
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.

Demo: https://youtu.be/6MYxJe6P4oY

Aerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.

I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.

Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.

There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Claude, open source tools including the Pi coding agent or Open WebUI, or integrate with applications built and maintained by individual organizations.

Almost no aerospace & defense company lives in all three major GovClouds (AWS, Azure, Google). When I mapped where frontier models can legally run for ITAR and CUI work, each provider had some subset of models, and each model achieved varying levels of compliance. Our customers don’t think in terms of cloud providers. They think about the data controls they require, and the model they want to use. That is why we built the compliance suffix. Under the hood, every request hits a single OpenAI-compatible endpoint, where keys are verified and policy is enforced before anything touches inference. Every model name must carry a compliance suffix, such as gpt-5.6-sol:fedramp-high or claude-opus-5-5:itar, and any bare model name is rejected. We never proxy to the standard commercial endpoints.

Our customers are also able to build an allowlist of models, and assign spend & model restrictions to individual users in the organization. This is all handled through our Consus Portal where teams can manage spend, keys, and policies.

A typical question we hear is why not just use Bedrock in AWS GovCloud or Azure Government directly. For small teams, just getting access to these platforms is difficult. For larger organizations, GovCloud lets you inherit some but not all of the controls needed to handle sensitive data. Also, models are split across providers: GPT-5.6 Sol is only available in Azure Government, while Claude Opus 5.5 is served through Bedrock in AWS GovCloud. If you want both, you are standing up and securing two separate government cloud environments. We run all of them behind one endpoint so customers don’t have to pick a cloud, and we handle the compliance work for each one.

Given the data sensitivities, Consus does not store any customer data at rest. All prompts and completions stay in memory only for the length of the request and are never logged or written to disk. Zero data retention is strictly enforced both on ourselves and the infrastructure/model providers. We do keep metadata on each request and response for billing and audit purposes.

Consus is not self-serve by design. ITAR requires us to know exactly who is on the other end of every key, so we verify every organization and put a contract in place before issuing API keys. We charge our customers a compliance fee starting at $40/mo/seat plus AI tokens at cost.

We are actively serving customers, and would love any feedback from security experts, or developers/engineers working in highly regulated industries!

References: API Documentation: https://gateway.consus.io/overview Security & Architecture: https://gateway.consus.io/security Compliance Details: https://consus.io/compliance



The compliance-suffix routing is a clean design choice — policy enforced in the API contract instead of buried in dashboard config. The hard part of any "zero data retention" gateway is observability, though: prompts routinely leak into error logs, APM traces, and dead-letter queues even when the happy path retains nothing. I'd want to know how redaction is handled there, because a single unredacted exception in a third-party error tracker can quietly undo the whole compliance story.

I love this. Will this guarantee that proprietary/ sensitive/ CUI data won't leak to model training or other parties?

Correct. We have contractual agreements to ensure no data that passes through our platform will be used for model training purposes. In addition, we have in our contracts that we (Consus) will also not use any data for training purposes. We couldn't if we wanted to since we do not store any prompt data.



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