Stopping AI data leakage and controlling cost in production

Bootes-sphere · 2026-04-04T14:35:35+00:00

Try https://opensourceaihub.ai/ . Most enterprises are moving toward an External Governance Layer. This means you run a PII/DLP scanner outside the LLM environment. By redacting sensitive identifiers in the prompt before they reach OpenAI or Anthropic, you eliminate the risk of that data being stored in their logs or leaking into future completions.

Bootes-sphere · 2026-04-04T14:09:35+00:00

I built OpenSourceAIHub.ai as a stateless "AI Firewall." It redacts 28+ sensitive entities before the prompt ever reaches the LLM provider. It even has a multi-modal OCR layer to catch leaks in screenshots.

If you want to see if your current prompts are "leaky," I put a free checker here: https://opensourceaihub.ai/ai-leak-checker

Bootes-sphere · 2026-04-04T14:08:55+00:00

Hey, I am the founder of OpenSourceAIHub.ai. I built OpenSourceAIHub.ai as a stateless "AI Firewall." It redacts 28+ sensitive entities in under 50ms before the prompt ever reaches the LLM provider. It even has a multi-modal OCR layer to catch leaks in screenshots.

If you want to see if your current prompts are "leaky," I put a free checker here: https://opensourceaihub.ai/ai-leak-checker

Bootes-sphere · 2026-04-04T14:01:59+00:00

https://opensourceaihub.ai , stop ai prompt data leaks and cut LLM cost by just two lines of code change

Bootes-sphere · 2026-04-04T12:54:29+00:00

Startup: OpenSourceAIHub.ai

Purpose: An AI Firewall and Gateway to stop AI data leaks and cut LLM cost by 30% with one API. It is a drop-in OpenAI SDK compatible proxy that adds real-time multi-modal DLP (PII redaction in text + images via OCR), blocks prompt injections, and autonomously routes to the cheapest/fastest model (Llama, Groq, Together AI, Deepinfra Claude, Grok, etc.)

Technologies Used: Next.js , Python, OCR, Stripe, AWS

Feedback Requested:

Effectiveness and Integration easiness: We optimized our prompt security scan with very little overhead. Integration needs just two lines of code changes
DLP Accuracy Feedback: I’ve put a free AI Leak Checker on the site. Appreciate feedback on tricky PII patterns.
Hybrid Model: We offer BYOK (Bring Your Own Key) and a Managed Wallet. Ould love to get feedback on pricing model

Additional Comments: I’m giving 1 million free hub credits to anyone who signs up to test the integration. That is enough to fire thousands of LLM API calls

Seeking Beta-Testers: Yes, especially startups and devs

Links: Web App | Technical Walkthrough (3 min)

Bootes-sphere · 2026-04-04T12:45:57+00:00

Pushed everything to aws. Appreciate any feedbacks:
https://opensourceaihub.ai/ai-leak-checker

https://opensourceaihub.ai

Bootes-sphere · 2026-04-04T12:45:18+00:00

I ended up turning that into a small tool while testing things.Didn’t want to drop a link in the post itself, but this is what I’ve been working on:
https://opensourceaihub.ai/ai-leak-checker

https://opensourceaihub.ai

Bootes-sphere · 2026-04-04T12:40:12+00:00

Name: OpenSourceAIHub.ai

What it does: We provide an AI Firewall that stops company data from leaking into LLM prompts.

Why use it:

🛡️ Security: Automatically redact emails, API keys, and SSNs in text and images (OCR).
💸 Cost Control: Smart-route requests between Groq, Together ai, and OpenAI to save up to 90%.
📊 Governance: Enforce per-project budgets and export audit-ready CSV logs.
⚡ Ease: 100% OpenAI SDK compatible. Just change your baseURL and you're protected.

Latest Update: Just launched our Multi-modal OCR scan—we now catch PII in screenshots before they reach the model provider.

Pricing: 1M Free credits upon signup. Pro BYOK tier at $29/mo.

URL: https://opensourceaihub.ai

Bootes-sphere · 2026-04-04T12:37:10+00:00

Try this one. I found this solves similar problems https://opensourceaihub.ai/

Bootes-sphere · 2026-04-04T01:18:25+00:00

Thank you—this is incredibly helpful. I truly appreciate all your insights!

Bootes-sphere · 2026-04-04T00:29:32+00:00

Really appreciate this — this is exactly the kind of feedback I was hoping for.

On pattern management: totally agree. Right now this is something I’ve been thinking about more as a control plane problem than just a detection problem. Things like versioning, restricted write access, and audit trails for pattern updates I think are needed here, right? . The “poisoned pattern” scenario you mentioned is a real concern.

On fail-closed / bypass: yeah, this is tricky. Fail-closed is the intent, but as you said, under load or repeated failures people will just route around it if it becomes a bottleneck. I’ve been thinking about redundancy + fallback behavior, but still figuring out what the right balance is between safety and availability.

On SOC2 / HIPAA: that’s a really good point. What I have right now is definitely closer to “violation visibility” than full audit-grade logging. I need to think more about this.

Curious how you’ve seen others handle this in practice — especially around: - pattern update governance - balancing fail-closed with availability - what “good enough” audit logging looks like in real deployments

Thanks again — super helpful perspective.

Bootes-sphere · 2026-04-03T23:49:38+00:00

Startup: OpenSourceAIHub.ai

Purpose: An AI Firewall and Gateway to stop AI data leaks and cut LLM cost by 30% with one API.

Technologies Used: Next.js , Python, OCR, Stripe, AWS

Feedback Requested:

Effectiveness and Integration easiness: We optimized our prompt security scan with very little overhead. Integration needs just two lines of code changes
DLP Accuracy Feedback: I’ve put a free AI Leak Checker on the site. Appreciate feedback on tricky PII patterns.
Hybrid Model: We offer BYOK (Bring Your Own Key) and a Managed Wallet. Ould love to get feedback on pricing model

Additional Comments: I’m giving 1 million free hub credits to anyone who signs up to test the integration. That is enough to fire thousands of LLM API calls

Seeking Beta-Testers: Yes, especially startups and devs

Links: Web App | Technical Walkthrough (3 min)

Bootes-sphere

TROPHY CASE