A Chilling Story About Why Companies Are Increasingly Focusing on Private LLMs

Hello, Mugiwara~♪

Thank you so much for coming to Mugi-ya's note (´∀｀)

Today, I read an article from the Forbes Business Council that really moved me, and I have something I want to share with you all!

Actually, did you know that companies are focusing on private LLMs? After reading this article, I felt a chill down my spine, thinking, 'Oh no... I might have been doing something dangerous.'

The incident that day that made me panic

First, I'll make a confession.

Around last spring, I was trying to streamline our back-office operations, so I was inputting internal company information into ChatGPT left and right.

'It's convenient, everyone is using it, so it must be fine!'

I thought about it too lightly...

But one day, a senior colleague from the IT department asked me, 'Mugi-san, what are you putting into ChatGPT?' and I was taken aback.

'Uh... just regular accounting data, contract information, and so on...'

My colleague's face clouded over.

'What plan are you using for that? With the personal version, depending on the settings, conversations can sometimes be used to improve the model.'

At that moment, my heart nearly stopped.

I was using the free personal version. I hadn't even checked the data control settings... I was inputting confidential company information without a second thought... It was exactly the kind of thing they warned us about in security training: 'Be careful with how you handle business data'...

I remember my blood running cold and my hands shaking for a while.

Are you all okay? Have you properly checked the AI plans and settings you are using?

An important trend reported by the Forbes Business Council

Because of this experience, the Forbes Business Council article really hit home for me.

According to an article written by Tyler Hochman, Founder & CEO of FORE Enterprise, there is a growing need for dedicated corporate environments due to governance requirements and other factors.is what it says.

Some of you might be wondering what a private LLM is, but simply put:

• AI that runs in a company-exclusive environment (dedicated tenant, VPC, on-premises, etc.)

• Easier to control in a dedicated environment, allowing for minimized access scope

• Designed for easier management of data handling

In other words, unlike public LLMs like ChatGPT (general-purpose AI that everyone can use), it is an AI that companies can manage in a dedicated environment!

However, it cannot be said that it "never goes outside at all." Factors such as configuration, operation, outsourcing, auditing, and legal preservation mean that risks cannot be reduced to zero.

Why are companies paying attention to it now?

The reasons introduced in the article were incredibly convincing.

1. Data management issues

This is the one I almost got burned by...

When using cloud-based AI services, the information you input is processed on the service provider's infrastructure.

But the important thing here is that "being transmitted" and "being used for training/leaking to third parties" are separate issues!

For example, in the case of OpenAI:

• Personal plans: Content may be used for model improvement, but it can be opted out (you can turn off "Improve the model for everyone" via Data Controls or the privacy portal, etc.)

• Business/Enterprise/API: By default, customer content is not used for model improvement (training). However, there are exceptions if you explicitly opt-in.

*Even for the same "personal" plan, handling changes depending on settings and opt-outs. Conversely, even with Business/Enterprise, risks remain depending on internal regulations, retention settings, and integration partners.

I was using a personal plan and hadn't checked the settings, which is why my senior colleague was worried about me...

With a private LLM, it is easier to control in a dedicated environment such as a dedicated tenant or VPC, and it is easier to minimize external transmission and access scope.

2. Customization challenges

Public LLMs are centered on general knowledge in their standard state.

For example, standard models don't understand things like my company's unique workflow or technical terminology...

Of course, you can make it handle internal information using RAG (a mechanism for searching and referencing) or tool integration. However, in that case, you need a secure design that includes access control, logging, prompt injection countermeasures (defenses against attacks where malicious documents contain embedded 'instructions' that cause malfunctions), and control over output destinations.

Private LLMs can search and reference internal documents, and in some cases, be trained to provide answers specialized for that company apparently.

3. Potential for ROI (Return on Investment)

This was an impressive point in the article.

Public LLMs are certainly easy to use, but it seems that in many cases, they are 'difficult to use as-is' for solving specific corporate problems.

On the other hand, private LLMs have clear use cases, and once data preparation, KPI setting, evaluation sets, and operations are in place, it is easier to measure their effectiveness apparently.

The article mentioned the potential for results leading to revenue improvement, cost reduction, and increased productivity.

However, it's not that 'if you do it, you will definitely get results'; solid preparation and measurement design are prerequisites.

But it's not all good news...

Reading this far, you might think, 'Well, why doesn't everyone just use private LLMs?', but of course, there are challenges.

Trade-off 1: Initial investment tends to be high

Private LLMs tend to be expensive, especially when building them fully in-house on-premises.

Even with managed dedicated environments, governance and operational costs are added on.

Unlike quickly using off-the-shelf services, you need to build and maintain the environment, after all...

Budget issues are likely to be a major hurdle, especially for small and medium-sized enterprises.

Trade-off 2: Specializing for a purpose can sometimes reduce versatility

The article stated that while private LLMs are excellent at solving specific high-value problems, they may not be suitable for a wide range of issues.

This varies depending on the model size, the degree of tuning, and tool integration, so it cannot be said unconditionally, but it means that if you narrow down the use case too much, it may become difficult to use for other things.

Trade-off 3: High-quality internal data is essential

This is also a super important point!

To utilize a private LLM, you need properly organized internal data.

Whether you are training it or using it with RAG (Retrieval-Augmented Generation), data quality determines the quality of the results.

For example, if a financial firm wants to build an AI for deal evaluation, they say it's easier to achieve accuracy and verification if they have past cases that include not just successes but also failures, along with the rationale for evaluations (labels, reviews, decision logs, etc.).

Without such "examples and evaluation criteria," the AI doesn't know what the correct answer is, does it?

Lessons for individuals and those using AI for side hustles

I've been talking about companies up to this point, but I don't want you to think, "This has nothing to do with us as individuals!"

It has everything to do with you.

Lesson 1: Check the plans, contracts, and settings you are using

Don't be like me and just use it without thinking because it's "convenient"!

Things to check first:

• Is it for personal use or business use?

• What are the data control settings? (Is "Improve the model for everyone" on or off?)

• How is input data handled? (Is it used for training? Is it saved? What about logs?)

• Is the tool permitted by company policy?

• What are the contract terms and data retention policies?

Especially when handling confidential information, please be sure to check in advance!

Lesson 2: Mask or anonymize before inputting

Even if you write "do not mention personal information" in your prompt, the data has already been sent the moment you input it.

Therefore, what is truly important is:

• Do not input confidential information in the first place

• If necessary, mask or anonymize data before inputting it

• Use only tools and plans authorized by the company (internal policy takes priority!)

• Utilize DLP (Data Loss Prevention) tools and gateways

• Don't forget about logging, retention, sharing, and auditing

• Be careful of misconfigured internal permissions, log sharing, and leaks via integrated tools

The fundamental rule is not to 'control the output,' but to 'control the input.'

Lesson 3: Understand the limitations of AI

Public LLMs are certainly convenient, but they are 'not omnipotent.'

They can answer general questions, but they cannot perfectly solve the unique challenges of your business.

Therefore, I think it is important not to expect too much from AI and to use it as an 'auxiliary tool.'

I have also had the experience of failing in the past because I expected 'AI to do everything!'...

Lesson 4: Preparation with an eye on the future

While it is not yet realistic for individuals to have their own private LLMs, that may change in the future.

We are already seeing examples of light customization, such as personalized GPTs (custom settings and tool integrations).

I thought it might be a good idea to start organizing data now with an awareness of 'AI specialized for your own business!'

Three actions you can take starting today

So, what exactly should you do? I'd like to share three things that I am practicing myself.

1. Create a security checklist

Clearly define what information is okay to put into AI and what is not.

I established a rule: 'Client names, monetary amounts, and personal information are strictly forbidden,' and 'Anonymize or mask data before use.'

2. Check the terms and settings of the AI services you are using

This is incredibly important!

• Is it for individual or corporate use?

• Data control settings (Is it used for training or not?)

• How is the data handled (What about retention periods, logs, sharing, and audits?)

• Is it permitted under company policy?

Especially when using it for work, it is safest to check with your IT department or supervisor before using it.

3. Use multiple AIs for different purposes (but be cautious with all of them)

It is also recommended to use different AIs depending on the task.

• General research → ChatGPT (Business plan or higher + check internal policies)

• When deep thinking is required → Claude

• Researching the latest information → Perplexity

However, with any tool, there is a possibility that input data may be sent externally, and the aspects of logs, retention, sharing, and audits are also important. Please do not enter confidential information, or only use corporate plans and tools that have been approved internally.

Even with Business/Enterprise plans, if your internal policy has operational rules such as 'Search use is okay, but entering confidential information is not,' please prioritize those!

Finally: Let's survive the AI era together

After reading this Forbes Business Council article, I realized something once again.

AI is convenient, but it is dangerous if you don't understand and use it correctly—that is the reality.

But there is no need to be afraid.

If you have the proper knowledge, check your contracts and plans, review your settings, and use it with security awareness, AI can become a truly powerful ally♪

I believe that by understanding why companies are focusing on private LLMs, we as individuals can also see how we should engage with AI.

I hope that after reading this article, you will feel encouraged to at least check the plans and settings of the AI you are using!

How are you all handling AI security?

If you have had a similar experience, please let me know in the comments.
Let's find better ways together♪

If you found this article even a little bit helpful,
I would be happy if you could leave a like or a comment!

Mugi🌾

Reference:
Forbes Business Council, "Why Businesses Are Rethinking Traditional LLMs" by Tyler Hochman (February 5, 2026)
https://www.forbes.com/councils/forbesbusinesscouncil/2026/02/05/why-businesses-are-rethinking-traditional-llms/

OpenAI, "Introducing ChatGPT Enterprise"
https://openai.com/index/introducing-chatgpt-enterprise/

OpenAI, "Enterprise privacy at OpenAI"
https://openai.com/enterprise-privacy/

OpenAI, "Business data privacy, security, and compliance"
https://openai.com/business-data/

OpenAI Help Center, "Data Usage for Consumer Services FAQ"
https://help.openai.com/en/articles/7039943-data-usage-for-consumer-services-faq

OpenAI Help Center, "How your data is used to improve model performance"
https://help.openai.com/en/articles/5722486-how-your-data-is-used-to-improve-model-performance

OpenAI Help Center, "Data Controls FAQ"
https://help.openai.com/en/articles/7730893-data-controls-faq

Note:
This article is for general information purposes only. For specific contract details or legal judgments, please be sure to consult with a professional or your organization. Data handling for AI services varies depending on the plan, contract, settings, and usage.

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