Can I Confide My Secrets to AI? On Safe AI Usage

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By Gi Pyeong Lee
AI Privacy Local LLM Data Security
Digital art representing a private conversation taking place on a computer screen
AI Summary

To avoid the risk of conversation history exposure in cloud-based AI, there is growing interest in methods to protect privacy by running AI models directly on your own computer hardware.

Imagine this: tonight, after much deliberation, you decide to confide in an AI chatbot about a problem you are embarrassed to tell others, or perhaps a sensitive project idea from work. You think to yourself, “No one will see this, right?” But were you aware that, technically, those precious questions you sent could be stored on a server somewhere in plain text, with the possibility of being viewed or recorded by someone?

With the recent advancement of AI technology, we are learning new things and improving ourselves every day, but concerns are growing that we are unintentionally handing over sensitive information to external servers in the process (Ask HN: How to ask questions to LLMs privately?).

Why is this important?

Things we never imagined have become reality. Last summer, a shocking incident occurred where some personal conversations held with ChatGPT were exposed in Google searches, making them accessible to anyone in the world (Figuring out LLMs, one (ideally private) chat at a time, and …).

The questions we casually throw at AI are not just simple data. They contain our personal information, professional secrets, and even emotional confessions. Currently, most cloud AI services often store the messages exchanged by users in ‘plain text’ (unencrypted original text) on their servers, leaving them fully exposed to the risk of external access or data storage (Mind the Trust Gap: Fast, Private Local-to-Cloud LLM Chat).

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The Explainer

Let’s compare the way AI handles data to a ‘letter.’

Using cloud AI is like sending a letter containing your secrets to a large, anonymous post office (cloud server). A post office worker reads the letter and writes a reply. It is convenient, but you never know if someone might steal a peek at your letter in between or store it away in a warehouse.

On the other hand, a Local LLM (Local Large Language Model, an AI model that runs directly on your computer) is like having a ‘personal AI tutor’ in your room. All conversations never leave your room. Because it thinks and answers only within your computer’s hardware even if the internet connection is cut off, there is simply no hole for data to leak externally (Best LocalLLMsforPrivatePersonal Conversations).

To use an analogy, if cloud models are like borrowing books from a large library where data from all over the world is collected, local models are like building a small personal study in your home. Of course, building that study requires a bit of technical effort, such as considering the size of the room (computer hardware performance) and arranging the furniture (precise prompt design) (Building a Private AI Assistant with Local LLMs — A Practical …).

Where We Stand

Many people who realize the importance of privacy are choosing to run AI directly on their own computers. However, local models are not a silver bullet for everyone.

While local models are very safe in terms of privacy, they require high-performance computers and sometimes involve the complexity of users having to adjust settings themselves. To solve this problem, cloud companies are also developing technologies like ‘TEE (Trusted Execution Environment).’ This is a method where data is only decrypted and processed within a remote environment where security is guaranteed, rather than externally (Mind the Trust Gap: Fast, Private Local-to-Cloud LLM Chat).

Currently, attempts are actively being made to process data privately on personal computers without complex technical knowledge using tools like ‘AnythingLLM’ ([AnythingLLM The all-in-one AI application for everyone](https://anythingllm.com/)).

What’s Next

We expect development to move in two directions. First is the advancement of hardware. It will become possible to run much lighter yet smarter models on regular PCs. Second is the strengthening of security policies. Companies will face fierce competition, having to attract customers by guaranteeing user privacy.

We hope readers will also think at least once about “Will this information remain on the server?” when using AI. The most important thing is to become a wise user who protects their precious information themselves, rather than blindly believing in AI as an ‘absolute authority’ (Ask HN: How to deal with people who trust LLMs?, Ask HN: How Do You Deal With People Who Trust LLMs?).

AI’s Take

Technology makes our lives convenient, but in exchange for that convenience, we are handing over data to places we cannot see. Building an AI that operates safely within my computer—that is, an ‘AI where my sovereignty is alive’—will become a basic skill for the future, not an option.

References

  1. HowIuseLLMs- YouTube
  2. Best LocalLLMsforPrivatePersonal Conversations
  3. Here’showIuseLLMsto help me write code
  4. [HowtouseANY AIprivately- The mostprivateLLM The Hated One](https://discuss.privacyguides.net/t/how-to-use-any-ai-privately-the-most-private-llm-the-hated-one/22605)
  5. [AskHN:HowdoyouuseLLMsto make life easier? Hacker News](https://news.ycombinator.com/item?id=43187050)
  6. [Ask HN: How to ask questions to LLMs privately? Hacker News](https://news.ycombinator.com/item?id=44738423)
  7. Building a Private AI Assistant with Local LLMs — A Practical …
  8. [Ask HN: How do you use Local LLMs? (April 2026) Hacker News](https://news.ycombinator.com/item?id=47816187)
  9. Mind the Trust Gap: Fast, Private Local-to-Cloud LLM Chat
  10. How to Use LLM with Private Data Best Practices for Data Security
  11. [How to Build a Private LLM: A Complete Guide Airbyte](https://airbyte.com/data-engineering-resources/how-to-build-a-private-llm)
  12. [Ask HN: How to deal with people who trust LLMs? Hacker News](https://news.ycombinator.com/item?id=47433702)
  13. [Ask HN: How are you using LLMs? Hacker News](https://news.ycombinator.com/item?id=44738424)
  14. Figuring out LLMs, one (ideally private) chat at a time, and …
  15. [AnythingLLM The all-in-one AI application for everyone](https://anythingllm.com/)
  16. [Ask HN HN Companion](https://app.hncompanion.com/ask)
  17. Ask HN: How Do You Deal With People Who Trust LLMs?
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Test Your Understanding
Q1. Which of the following is an example of a privacy risk mentioned when using cloud-based AI?
  • AI automatically deletes all emails
  • Personal conversation content exposed in Google searches
  • AI model hacks the user's computer
There have been cases where 'secret' conversations of some users were indexed by Google and made public on the internet.
Q2. What are the advantages of using a local LLM?
  • Always provides unlimited performance without an internet connection
  • Data is not sent to external servers, protecting privacy
  • Always smarter than cloud models
Local models run directly on the user's device without passing through external servers, eliminating the need to expose personal information externally.
Q3. What factors should be considered when building a local AI assistant?
  • Managing hardware constraints and prompt tuning
  • Sharing conversations with users worldwide
  • Setting up unlimited cloud storage
Utilizing local models requires technical effort, such as considering hardware performance, designing appropriate architecture, and setting up precise prompts.
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