In recent years, AI has quietly become a part of our everyday lives.
When we arrive at the office in the morning, we use AI to help organize yesterday’s meeting notes.
In the afternoon, while writing emails, we ask AI to polish the wording so it sounds more professional.
Before leaving work, we throw a difficult problem to AI to see if it can spark some inspiration.
We use AI to:
✍️ Write emails and refine resumes
📊 Organize meeting notes and create presentations
🧠 Research information and generate ideas
👨💻 Help write code and fix bugs
All of this feels completely natural and seamless—
so seamless that we rarely stop to think about one important question:
“Did I just hand sensitive data over to AI?”
Most of the time, what we pursue is efficiency:
Can we finish faster?
Can we spend less mental effort?
Can we get an answer that “looks good enough”?
Because of this, many people initially think:
“It’s just a tool. I’m not downloading a virus, so it should be fine, right?”
After all, we’re not browsing hacker forums,
nor are we installing suspicious software.
We’re simply pasting some text and asking a question.
But the real issue is this—
the risks of AI are often not about what you intentionally do,
but about what you unintentionally do.
The moment you start using AI, several things are already happening behind the scenes:
- You are inputting data into a system that is not fully under your control
- You are allowing an external system to access your content, text, or ideas
- You begin relying on AI-generated outputs to influence your judgment
And fundamentally, all of these behaviors are related to information security.
So the real question is not:
“Can we use AI?”
But rather:
👉 “In an era where everyone is using AI, are we using it securely enough?”
Next, let’s take a closer look at the information security risks AI brings—many of which you may not have noticed—and how we can avoid them.
🧩 1. What Is “AI Information Security”?
When we think of cybersecurity, we often picture:
Hackers 🧑💻
Ransomware 💥
Account breaches 🔓
But AI-related information security covers a much broader scope.
🧠 In simple terms, it comes down to three things:
Whenever you use AI, ask yourself these three questions:
Input: What data am I feeding into the system? (privacy and confidentiality)
Processing: Will this data be stored, analyzed, or even used to train future AI models? (ownership and control)
Output: Can I fully trust the answers AI provides? (accuracy and reliability)
If any one of these stages goes wrong, it could evolve into a cybersecurity issue for individuals or organizations.
🍽️ 2. Why Is Using AI Related to Information Security?
🧑🍳 A simple real-life analogy
Imagine handing an internal company document to a stranger—someone very smart—and asking them to summarize it.
You would naturally wonder:
🤔 Will they secretly keep a copy?
🤔 Will they share it with someone else?
🤔 Is their summary actually accurate?
AI is very much like that:
It’s highly capable, but you don’t always know how it handles your data behind the scenes.
⚠️ 3. The 5 Most Common AI Security Pitfalls
❌ 1. Pasting Confidential Data Directly into AI
Common scenarios include:
- Pasting unpublished contracts into AI for editing
- Uploading customer lists for analysis
- Feeding internal planning documents into free AI tools for summarization
📌 The key risk:
Once the data is submitted, it enters a cloud-based system and may even be used to train future models.
Taiwanese tech media TechOrange has pointed out that when companies adopt AI, unclear boundaries around data usage are one of the most easily overlooked sources of risk.
🔗 “Why Companies Must Rethink Security When Adopting AI: 5 Overlooked Risk Scenarios”
https://techorange.com/2025/04/16/ai-security-
👻 2. “Shadow AI”: You’re Using It, But Your Company Has No Idea
Shadow AI refers to:
Employees privately using unapproved or unknown AI tools for work-related tasks without the company’s knowledge or IT authorization.
For example:
- Using free AI tools to revise quotations
- Pasting internal data into AI translation tools
- Secretly using unknown generative AI platforms
📌 Why is this dangerous?
- IT and security teams have zero visibility
- Data flows cannot be controlled
- Incidents become difficult to trace
According to an official report by Palo Alto Networks, “Shadow AI” has become one of the newest and fastest-growing cybersecurity risks for enterprises.
🔗 Palo Alto Networks, “2025 Generative AI Security Report”
https://www.informationsecurity.com.tw/article/article_detail.aspx?aid=11996
(Compiled by iThome Taiwan)
🌀 3. AI Is Very Good at “Confidently Saying Things That Aren’t True” (AI Hallucination)
AI does not truly “understand” content—it generates responses based on probability. As a result, it may:
- Sound highly professional, but actually be incorrect
- Fabricate non-existent laws, data, or research cases
- Provide answers that are “plausible but wrong”
This phenomenon is known as:
AI Hallucination
📌 Key risk:
If incorrect outputs are used directly for decision-making, they may lead to legal liability or reputational damage.
The U.S. authority NIST (National Institute of Standards and Technology) explicitly states in its AI risk documentation:
“Over-reliance on AI outputs is itself a risk.”
🔗 NIST – Artificial Intelligence Risk Management Framework (AI RMF)
https://www.nist.gov/itl/ai-risk-management-framework
🎭 4. AI Makes Scams and Misinformation More Convincing
AI has made fraud significantly more sophisticated.
Hackers can use Deepfake technology to mimic a manager’s voice or appearance, or craft nearly flawless social engineering emails.
You may have seen news like:
- Fake boss voices requesting urgent fund transfers
- Deepfake executive video calls
- Highly customized phishing emails with almost no obvious flaws
📌 Key risk:
When fraudulent content becomes “too realistic,” human intuition and warning signals can fail.
Behind many of these cases is AI-powered deepfake technology.
CIO Taiwan has noted in its analysis of generative AI risks that AI is increasingly being used to enhance the precision and effectiveness of social engineering attacks.
🔗 “The Double-Edged Sword of Cybersecurity: Three Major Risks and Opportunities of Generative AI” | CIO Taiwan
https://www.cio.com.tw/102645/
🔗 5. AI Tools, Plugins, and Models Themselves May Also Pose Risks
Many users install AI plugins or Chrome extensions, but the security quality of these tools varies widely.
The AI tools you use often integrate with:
- Third-party services
- External data sources
- Plugins and APIs
📌 Key risk:
Insecure plugins can become backdoors for data leakage.
The international nonprofit cybersecurity organization OWASP has identified:
The Top 10 risks in AI applications, including prompt injection, data leakage, and over-reliance on outputs.
🔗 OWASP – Top 10 for Large Language Model Applications
https://owasp.org/www-project-top-10-for-large-language-model-applications/
✅ 4. How Can Ordinary Users Use AI Safely?
The good news is:
👉 You don’t need to be an engineer to achieve 80% of effective protection.
Build the following mindset:
🛡️ 8 Practical Principles for Everyday Users
- De-identify data: When editing content, remove specific names, company details, and financial figures
- Treat AI as an assistant: Not a decision-maker—always perform human review
- Use enterprise versions: If available, prioritize paid enterprise AI tools with stronger privacy protections
- Verify facts: Always double-check data, references, and legal citations provided by AI
- Stay alert to anomalies: For highly realistic voice/video requests involving money or authorization, confirm via a second channel
- Follow policies: Comply with your organization’s AI usage guidelines
- Review permissions: Before installing AI plugins, verify the developer’s credibility and avoid granting excessive permissions
- Final self-check: Before clicking “send,” ask yourself:
“If this text appeared on tomorrow’s front-page news, could I handle the consequences?”
🌱 5. Conclusion: Security Is a Core Literacy in the AI Era
AI is like an extremely sharp double-edged sword 🔪:
- Used well, it dramatically boosts efficiency
- Used poorly, it creates significant risk
Information security is no longer just the responsibility of IT departments—it is a fundamental skill for every AI user.
In a world where everyone is chasing AI-driven productivity, only secure usage can ensure that AI becomes a sustainable and powerful long-term ally.
Cybersecurity is not just an engineer’s responsibility—it is a basic competency for every user.
In the AI era,
knowing how to use AI is important,
but using AI securely is even more important.
