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Best practices for using public LLMs with sensitive cloud data?

Posted on January 29, 2026
Rom c

By Rom c

Founder of Questa AI

Hi everyone,

I’ve recently started using public LLM tools (like ChatGPT and similar AI assistants) to help with tasks such as:

  • debugging scripts
  • reviewing configs
  • analyzing server logs
  • generating infrastructure commands

They’re really helpful for productivity, but I’m concerned about the security side when working with cloud environments.

Sometimes logs, configs, or snippets may contain:

  • IP addresses
  • API keys/tokens
  • database details
  • customer or production data

Uploading that to a public AI tool feels risky from a privacy and compliance perspective.

For those working with DigitalOcean or other cloud platforms:

What’s considered best practice here?

Do you:

  • sanitize/anonymize data first?
  • use only mock data?
  • rely on internal/self-hosted AI tools?
  • or avoid pasting sensitive info entirely?

Would love to hear how others handle this safely in real-world workflows. Thanks!



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Hi there,

For me, a good rule of thumb is to treat public LLMs like a public pastebin. If you would not paste it on the internet, do not paste it into an AI tool.

What most people do in practice:

  • Sanitize first: redact IPs, tokens, customer data, hostnames, etc.

  • Use mock or representative configs instead of real production ones

  • Avoid pasting full prod logs or secrets entirely

For more sensitive workloads, running models in your own environment is the safest route. On DigitalOcean, the Gradient platform lets you work with LLMs inside your own cloud setup, so data does not leave your control: https://www.digitalocean.com/products/gradient/platform

Public LLMs are great for speed and productivity, just use them with the same caution you would use when sharing anything publicly.

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