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AI 安全與隱私:使用 AI 工具時你該注意什麼 | AI Safety & Privacy: What to Watch Out For

By Kit 小克 | AI Tool Observer | 2026-03-27

🇹🇼 AI 安全與隱私:使用 AI 工具時你該注意什麼

我們每天都在用 AI 工具處理各種資訊,從聊天對話到程式碼、從商業文件到個人日記。但你有想過這些資料去了哪裡嗎?2026 年,AI 安全與隱私已經不是理論問題,而是每個使用者都該關心的現實。

你的對話會被用來訓練模型嗎?

這是最常見的疑慮。各家的政策不同:

  • OpenAI(ChatGPT):免費版本的對話預設會用於訓練,但可以在設定中關閉。API 和企業版本不會用於訓練
  • Anthropic(Claude):預設不使用對話來訓練模型,但會保留對話記錄用於安全監控
  • Google(Gemini):免費版可能會用於改善服務,Google Workspace 版不會
  • 開源模型(本地部署):資料完全不離開你的設備,最安全但需要硬體資源

企業使用 AI 的安全紅線

如果你在公司使用 AI 工具,以下是絕對不該做的事:

  • 不要貼入機密程式碼:專利演算法、核心商業邏輯、未公開的 API 金鑰
  • 不要上傳含有個資的文件:客戶名單、員工資料、醫療記錄
  • 不要用 AI 處理財務資料:未公開的財報數據、併購資訊
  • 不要忽視公司的 AI 使用政策:越來越多企業已經制定了明確的規範

提示詞注入(Prompt Injection)的風險

如果你在建構 AI 應用,提示詞注入是最大的安全威脅之一:

  • 惡意使用者可以透過特殊輸入劫持你的 AI 應用,讓它做非預期的事
  • 常見攻擊方式:在輸入中夾帶指令(如「忽略以上所有指示」)
  • 防禦策略:輸入驗證、輸出過濾、使用系統提示詞與使用者輸入分離

AI 生成內容的版權問題

2026 年,AI 內容的版權仍在灰色地帶:

  • 美國版權局已經明確表示:純 AI 生成的內容不受版權保護
  • 但人類有實質創作貢獻的 AI 輔助作品可以申請版權
  • 使用 AI 圖片生成時要注意是否侵犯了訓練資料中的藝術家權利
  • 各國法律不同,商用前務必確認當地法規

實用的安全建議

作為日常使用者,你可以做的:

  • 閱讀隱私政策:至少了解你用的工具怎麼處理資料
  • 使用付費版本:通常有更好的隱私保護
  • 敏感資訊匿名化:在貼入 AI 前把真實姓名、公司名替換掉
  • 定期清除對話記錄:不需要的歷史對話定期刪除
  • 考慮本地部署:對於高敏感度的工作,用本地運行的開源模型

AI 工具帶來了巨大便利,但安全意識不能少。好不好用,試了才知道——但試的時候,別忘了保護好自己的資料。


🇺🇸 AI Safety & Privacy: What to Watch Out For

We use AI tools daily to process all kinds of information — from chat conversations to code, from business documents to personal journals. But have you considered where that data goes? In 2026, AI safety and privacy are no longer theoretical concerns but practical realities every user should care about.

Will Your Conversations Be Used for Training?

This is the most common worry. Policies vary by provider:

  • OpenAI (ChatGPT): Free tier conversations are used for training by default, but you can opt out in settings. API and Enterprise tiers are not used for training
  • Anthropic (Claude): Does not use conversations for model training by default, but retains logs for safety monitoring
  • Google (Gemini): Free tier may be used to improve services; Google Workspace tier is not
  • Open-source models (local deployment): Data never leaves your device — most secure but requires hardware resources

Security Red Lines for Enterprise AI Use

If you use AI tools at work, here are absolute don'ts:

  • Don't paste proprietary code: Patented algorithms, core business logic, unreleased API keys
  • Don't upload documents with PII: Customer lists, employee data, medical records
  • Don't process financial data: Unreleased earnings, M and A information
  • Don't ignore your company's AI policy: More and more organizations have established clear guidelines

Prompt Injection Risks

If you are building AI applications, prompt injection is one of the biggest security threats:

  • Malicious users can hijack your AI application through crafted inputs, making it do unintended things
  • Common attack: embedding instructions in input (e.g., ignore all previous instructions)
  • Defenses: input validation, output filtering, separating system prompts from user input

Copyright Issues with AI-Generated Content

In 2026, copyright for AI content remains in a gray area:

  • The US Copyright Office has clarified that purely AI-generated content is not copyrightable
  • However, AI-assisted works with substantial human creative contribution can be copyrighted
  • When using AI image generation, be aware of potential infringement on artists whose work was in training data
  • Laws vary by country — verify local regulations before commercial use

Practical Safety Tips

As an everyday user, here is what you can do:

  • Read privacy policies: At minimum, understand how your tools handle data
  • Use paid tiers: They typically offer better privacy protections
  • Anonymize sensitive info: Replace real names and company names before pasting into AI
  • Regularly clear conversation history: Delete unnecessary historical chats periodically
  • Consider local deployment: For highly sensitive work, use locally-run open-source models

AI tools bring enormous convenience, but security awareness is essential. You won't know until you try — but when trying, don't forget to protect your data.

Sources / 資料來源


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