YHK - yet another hacker news clone

36 points ericmags 4 days ago 20 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

runamuck 4 days ago | parent

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

ericmags 4 days ago | parent

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.

projectescape 4 days ago | parent

This is great, question though, since you support coding agents like Claude Code and Codex, they run MCP servers, plugins, web search/fetch and their own telemetry from the dev's machine. Do you give customers guidance or controls for that side, or is it out of scope for the gateway?

ericmags 4 days ago | parent

We do give our customers guidance to make sure they are thinking about this properly. This typically happens during on-boarding.

We also built out pre-defined controls for common tools so they can click a button and not think about it.

projectescape 4 days ago | parent

Food for thought, tool and MCP calls run locally, but every request carries the tool list and every response carries the model's tool calls, both of which pass through you. You could strip non-approved tools from the request and block calls to them in the response, based on an org allowlist. That gives you org-approved MCP/tool calls without touching the dev's machine.

ericmags 4 days ago | parent

Great call. We already block certain tool schemas and flag URLs in tool-call arguments, and since we enforce org allowlists for models on every request, extending that to tools/MCP is a natural next step.

liquado 3 days ago | parent

We had a lot of problems letting finding a balance giving technical coworkers acces to live data and AI tools. And there are multiple companies in this space that solve things in multiple ways, we happen to use https://gluondb.com which is what you describe plus BI.

skinfaxi 3 days ago | parent

Seems like a pretty crowded space.

digitalfortress 3 days ago | parent

To build on this: a gateway controls where the model request goes, but with coding agents the data often leaves another way. A tool result can include file contents, and then the model can be steered into putting that data into a web fetch URL or a call to some third-party MCP server. That traffic never goes through the gateway, because it starts from the client machine. So the real boundary has to include an egress allowlist on the developer's machine or in its sandbox, not just the model endpoint. I'd be curious whether Consus ships a recommended client-side configuration, or treats that as the customer's job.

skinfaxi 4 days ago | parent

How does ZDR work with requests that trip security measures in the model provider? Do the providers retain those requests for analysis or is that too guaranteed?

ericmags 4 days ago | parent

The infra providers call this "abuse monitoring" or "abuse detection" and we've worked with them to turn it off. We don't serve models like Fable that require it. We have one case remaining for OpenAI's GPT-5.x models in AWS. AWS's automated classifier can retain a flagged request for up to 30 days. Per AWS, that review is automated with no human access, the data stays in AWS GovCloud, and it's never shared with OpenAI. We are actively working to turn that off as well. Everything else is covered.

jayonsoftware 4 days ago | parent

Does this work with HIPAA data ?

ericmags 4 days ago | parent

We've been focused on defense, so we haven't set up HIPAA (BAAs) yet. The underlying controls are essentially FedRAMP High, so it wouldn't be a big lift on our side. If you have a specific use case in mind, drop me a note at founders@consusindustries.co and we can talk more about it.

popkoren 3 days ago | parent

Compliance at the gateway is necessary, but the apps those teams deploy still need the same skepticism on the other side of the fence. Auth bypasses, open storage and loose table rules do not show up in a FedRAMP checklist the way a missing control on the model path does. What has helped on small stacks is hitting the live URL as an anonymous visitor and as a second user. I built https://rowly.me to run that pass on AI-built apps automatically (sign-in, APIs, storage, database rules).

armegaddon 3 days ago | parent

Interesting that the pitch is density: $0.37/month per sandbox implies a lot of them packed onto shared hardware. My sandbox experience is the malware kind: at Fortinet I ran NSS Labs testing on a sandbox engine, and a key question was whether results held as throughput tiers went up, with sample loss tracked per tier. Same question seems worth asking here: do isolation and agent performance hold at peak packing, not just average utilization?

pueding 3 days ago | parent

Does it offer a self-hosted or air-gapped option for customers who cannot send data to any outside service?

christopheromar 3 days ago | parent

These are some pretty significant compliance claims, especially around ITAR, CUI, and FedRAMP High equivalency. I think the technology sounds interesting, but I'd like to understand what's actually happening behind the scenes before taking those claims at face value.

A few things I'd be curious about:

- *ITAR compliance:* What does that mean in practice for Consus? Are there specific controls around personnel access, data residency, subcontractors, and handling export-controlled information?

- *FedRAMP equivalency:* Was there an independent assessment covering all 410 FedRAMP High controls, or is this based on internal control mapping? Also, is there any publicly available documentation showing the assessment scope or authorization status?

- *Data handling:* Where do prompts and responses go? Are they stored, and if so, for how long? Can customers disable retention? I'd also be interested in how logging works and whether downstream model providers can retain data for training.

- *Customer isolation:* Are customers running in separate environments with dedicated encryption keys, or is isolation handled logically within shared infrastructure?

- *Security enforcement:* How does the system handle prompt injection or attempts to generate restricted technical information? Is there an additional security layer beyond the model itself, and can customers audit those decisions?

- *Encryption and key management:* Who manages the encryption keys, how often are they rotated, and what safeguards prevent unauthorized access?

I understand that not every security detail can be made public, but even a high-level explanation of the assessment scope, control coverage, and how data moves through the system would help.

There's definitely value in making AI accessible for regulated environments. I just think the distinction between claiming compliance and demonstrating how it's enforced matters a lot, especially for organizations dealing with sensitive information.

Would be interested to hear how the team approaches these challenges.

peplee 1 day ago | parent

Cool solution, and kudos on investing in getting the compliance groundwork stuff sorted out early. I can't imagine that was cheap or easy. I'd think this is primarily interesting to the newer a&d companies, right? Or even companies who are looking at their first government project. That seems to me like where the value is. The usual suspects tend to already have people and policies in place for proper handling of sensitive info.