The top privacy concern when using generative AI is that it isn’t actually AI. Yes, you read that correctly.
What we now call artificial intelligence remains under active development. Generative AI systems and other AI technologies still show human biases and need human oversight. Developers continue to push advancements in AI at breakneck speed. Even so, we stay far from creating true AI.
As a result, companies and individuals who use today’s AI tools face real risks. They often unknowingly expose their data to human review. If you value your personal data, ask yourself how much trust you’re placing in these systems. Would you hand your information to a stranger without permission? Now consider your company’s proprietary data—would you feel secure letting an unknown person access your most sensitive information?
What is True Artificial Intelligence?
True AI, otherwise known as artificial general intelligence (AGI), is still bogged down in research and development. What is now in use and referred to as “AI” consists of generative AI algorithms, chatbots, and programs. These are designed to use data collection and machine learning algorithms. They generate content outputs based on programmed inputs or questions. Examples of generative AI include ChatGPT, Grok, DALL-E, and Google Gemini. While these emerging technologies can be quite useful, they still lack the features of true AI.
How is Generative AI Different?
According to an article on Forbes by Bernard Marr, generative AI differs from true AI in three key ways:
- Copying vs. Creating: Generative AI copies patterns from data. It makes content based on what it has seen before. It does not think or create new ideas. True AI would solve problems in new ways. It would think more like a human and come up with original ideas.
- No Understanding vs. Real Understanding: Generative AI does not understand what it makes. It only follows the instructions you give it. It doesn’t know the meaning behind the words. True AI would understand its surroundings. It could connect ideas that don’t seem related. It might even show signs of human-like thinking and wisdom.
- One Job vs. Any Job: Generative AI works best on one task at a time. People use it in certain jobs or industries. True AI would handle many kinds of tasks. It could work across different fields, just like a person who can think and adapt.
These differences all matter. But the biggest one is this: generative AI still needs humans to run it. People set the rules, give it input, and decide what counts as a good result. AI doesn’t work on its own—not yet.
True AI, on the other hand, could work by itself. It wouldn’t need humans to guide it. It would choose how to answer questions and check its own work.
Right now, many AI chatbots avoid answering certain questions. They use filters to block answers on controversial topics. This shows bias. Human programmers built that in.
Maxwell Zeff and Thomas Germain wrote about this issue in an article for Gizmodo. They looked deeper into how this bias works in generative AI.
Is Your Data Privacy At Risk When You Feed Proprietary Information to AI Models?
When a user interacts with generative AI or any other machine learning system, the information provided can fall into two categories:
- Training data: Historical information the model has been trained on typically gathered from a range of publicly available sources.
- User-input data: Information provided by users after the model is trained. This information may include a business’s proprietary documents or other sensitive content. In a healthcare setting, this may include protected health information. In law-enforcement settings, this could include biometric data.
This is the main question of this article. Can inputting sensitive data into a generative AI system lead to data breaches? Can it result in trade secret exposure, or consumer privacy concerns? The answer is yes, but thankfully there are several ways to avoid potential privacy risks.
Preventing Disclosure of Proprietary Information
To protect proprietary information, businesses need to be proactive when using generative AI models in the workplace.
1. Read Privacy Policies and Draft NDAs
Before using any AI model, businesses should review their AI provider’s privacy policies. Three particular things to look out for include:
- How is data handled?
- Is data stored?
- Is data used for further model training?
Whenever necessary, companies should draft non-disclosure agreements (NDAs) or data-handling contracts to ensure the confidentiality of sensitive information.
2. Avoid Inputting Sensitive Data
The best and easiest way to avoid the top privacy concern of using generative AI and stay safe is simple. Don’t put sensitive data into AI tools. Never use private or secret company data with AI. Instead, companies can hide details by making data anonymous. They can also use synthetic data. This data looks like the real thing but doesn’t show private info. By doing this, businesses still get the benefits of generative AI. At the same time, they must lower the risks of the top privacy concern when using generative AI.
3. Create Data Privacy Protocols
Companies need to set clear rules for using generative AI. They should write simple, easy-to-follow policies. Employees must learn the risks of using AI tools. They should know what they can and cannot do. Some good rules include: don’t enter sensitive data and only use secure, approved datasets. These steps help keep company information safe.
4. Monitor Usage
Companies should check which AI tools their employees use and why. They need to know how these tools are helping or creating risks. By keeping track of AI use, businesses can spot problems early. They can make sure no one is sharing private or secret company data by mistake.
Conclusion
AI tools help businesses work faster and make better choices. They boost real-time work and decision-making. But they also come with risks. These risks include leaks of private company info or personal data. To stay safe, companies must learn how AI handles and shares data. This helps lower the risk. They also need to keep a close eye on privacy and security.
The main point is simple: AI does not act alone. Humans still control it. That means people are also responsible for the risks and the safety measures.
See also:
History of Technical Writing: From Cave Walls to AI