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HardSaaSFrontierClear niche and buyerAI model/API accessPayments via Stripe
Verified

Automated Pen-Testing for AI Products

Audn automates external black-box penetration testing for AI companies and enterprises. Existing cyber models are trained mostly on open-source data,…

In plain English

Before an AI product is trusted with real users, Ozgur Ozkan's Audn tests it from the outside, without being handed the private inner workings. It looks for weak spots, such as private information leaking or the product being tricked, and writes reports tied to rulebooks companies are expected to follow. AI companies and larger firms pay because those holes often sit in the live product. Testing, report writing, and billing sit mostly with the AI. Ozgur still checks quality, talks with security teams, and handles the hardest unusual cases.

Automation85%
Startup costNot disclosed
Time to first $Not disclosed
Monthly range*$28,442 MRR

~$28,442 MRR; $52,242 verified revenue last 30 days (TrustMRR/Stripe, Oct 23, 2026)

DifficultyHard
SourceTrustMRR - Audn.AI ↗

Operator: Ozgur Ozkan

Verified

Revenue independently checked: documents reviewed by a publication, or a public dashboard.

Not disclosed.

The tool stack

Stripe ↗
2.9% + 30¢ per charge

Payments

 OpenAI API ↗
Pay per token

LLM / vision

 Next.js ↗
Free (open source)

Web app framework

Prices are the vendors' published list prices as researched in Oct 2026 and change often — always check the vendor's pricing page.

Step-by-step setup

  1. Automate black-box attacks (prompt injection, jailbreaks, data leaks).
  2. Generate reports mapped to compliance frameworks.
  3. Sell to AI startups and enterprises.
  4. Grow with security content and audits.

What's automated vs. the human 15%

The machine does

  • Core AI generation/analysis
  • Billing and onboarding

You still do

  • Product quality and support
  • Distribution and marketing
  • Compliance and edge cases

Overview

Audn automates external black-box penetration testing for AI companies and enterprises. Existing cyber models are trained mostly on open-source data, while the highest-risk vulnerabilities now sit in closed-source production systems like binaries, public endpoints, network misconfigurations, AI behaviours. We have early pentester usage and a paid pilot with a YC S25 voice AI startup that generated revenue tied to a retailer contract.

Operator: Ozgur Ozkan. Category on TrustMRR: Artificial Intelligence. Founded: 2025-08-21.

Realistic income

TrustMRR (synced Oct 23, 2026) shows about $28,442 MRR and $52,242 in Stripe-verified revenue over the last 30 days, $85,161 verified over its tracked history. This is one operator’s result, often after months or years of work, and doesn’t predict yours. Check the live listing — numbers change.

Risks

  • Needs real security expertise.
  • Long enterprise sales cycles.
  • Startup cost and time to first dollar are not disclosed.

*Income figures are illustrative ranges based on public pricing and typical outcomes reported by practitioners, not guarantees. Most new online businesses earn little or nothing at first. Not financial, legal or tax advice.

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