Note on method. In this article, every statement about the model refers back to OpenAI's page. When something is not written there, we present it as an open question and not as a fact. If a supplier promises you results, timelines or costs on this model, ask where they are documented.
## What do we know and what don't we know?
The table sharply separates what the source states from what is missing. It is the safest way to avoid attributing to the model qualities that nobody has documented yet.
| Appearance | What the source says | What remains to be clarified |
| --- | --- | --- |
| Purpose | GPT-6 Cyber is a model for cybersecurity ([source](https://openai.com/index/introducing-gpt-6-cyber)) | For which types of system and with what limits |
| Tasks | Finding exploitable vulnerabilities and developing fixes ([source](https://openai.com/index/introducing-gpt-6-cyber)) | How reliable the results are and how they are verified |
| Behavior | Trained to stay within the authorized perimeter ([source](https://openai.com/index/introducing-gpt-6-cyber)) | How the perimeter is defined and controlled |
| Access | Not indicated | Who can use it and under what conditions |
| Pricing | Not indicated | Cost, billing model, any usage limits |
| Availability | Not indicated | Timing and countries where it will be available |
The correct reading is simple: the direction is clear, the details are not. Those who decide on purchases or budgets should not rely on information the page does not provide.
## How could it serve an SME? Two hypothetical examples
The two scenarios below are examples built for illustration, not client stories or measured results. They help show where a model of this kind could fit in, assuming it becomes accessible.
**Example one: a professional firm with a website and a client portal.** A mid-sized consulting firm runs a showcase website and a restricted area where clients upload documents. The owner is not a technician and relies on an external supplier. In a hypothetical scenario, a cybersecurity tool could help the supplier check the restricted area and prepare a proposed fix for each weakness found. The owner would remain responsible for authorizing the check, deciding when to apply the changes and verifying that client data stays protected. On this last point it is worth reviewing [what the GDPR requires of companies that use AI](https://giallostudio.it/en/blog/ai-and-gdpr).
**Example two: an online shop with a custom management system.** An Italian business selling online uses a management system developed by a software house for orders and warehouse. Over time the code has been modified by several people and nobody has a complete picture. Hypothetically, a specialized model could assist the developer in analyzing the code and writing fixes. But a test environment, review by someone who knows the system and a backup before every change would remain indispensable.
In both cases the model would be support for the people doing the work, not a replacement. And in both cases the initial question is not technical: it is who authorizes what.
## What mistakes should you avoid when talking about AI for security?
There are five mistakes to avoid when a novelty like this reaches the news and the desks of business owners.
### Assuming the model is already available
It happens because an announcement looks like a launch. The source, however, says nothing about access, dates or conditions. Avoid it like this: until an official page with the terms of use exists, treat the tool as a development to follow, not as a purchase to plan.
### Attributing to the model performance nobody has stated
A title like cybersecurity suggests complete protection. [OpenAI's page](https://openai.com/index/introducing-gpt-6-cyber) talks about finding vulnerabilities and developing fixes, not about guaranteeing that a company is safe. Avoid it like this: write down separately what is documented and what you imagine, and do not mix them in internal documents.
### Leaving the perimeter undefined
It happens when you ask to check everything without listing the systems. Even a model trained to respect limits needs clear limits. Avoid it like this: before any check, make a written list of the systems included and excluded, with the name of whoever authorizes it.
### Applying fixes without review
A fix proposed by a model can solve one problem and create another. It happens when haste prevails over verification. Avoid it like this: every change goes through a test environment and the approval of a person who knows the system.
### Ignoring internal rules and the data involved
A security check can touch personal data, credentials or confidential documents. It happens when you think only about the tool and not about what it sees. Avoid it like this: write an internal usage rule, starting from the guide on how to set up a [corporate policy on the use of AI](https://giallostudio.it/en/blog/company-ai-use-policy).
## How can you prepare today, without waiting for the model?
The advantage of waiting methodically is that almost all the useful work does not depend on a specific tool. If one day a model like this becomes accessible, an SME that is already organized will be able to assess it better than one starting from scratch.