Artificial intelligence is a powerful tool for increasing productivity, but it comes with security risks that must be understood. From information compliance risks to hallucination and bias, there are several security challenges that employees and companies need to know before adopting these technologies.
Article contents:
- Introduction
- Information compliance risks
- Hallucination risks
- Bias risks
- Injection attacks and data poisoning
- User error
- Intellectual property infringement
- Unknown risks
- Best practices
- Conclusion
Introduction
Are you using artificial intelligence in your work yet? If you aren’t, you run a serious risk of falling behind your colleagues. Smart chatbots, AI image generators, and machine learning tools are powerful productivity boosters. But with great power comes great responsibility, and you need to understand the security risks of using artificial intelligence at work.
As a tech editor, I have found wonderful ways to use AI tools in my role. My favorite tools for professionals like Otter.ai, Grammarly, and ChatGPT have proven extremely useful for tasks such as recording interviews, taking meeting minutes, and quickly summarizing long files.
Information compliance risks
Do you have to sit through boring training every year about HIPAA compliance, or the requirements you face under the European GDPR? Then, theoretically, you should already know that violating these laws carries severe financial penalties for your company.
Mishandling customer or patient data could also cost you your job. Furthermore, you may have signed a non-disclosure agreement when you started your job. If you share any protected data with an external artificial intelligence tool like Claude or ChatGPT, you might be violating your non-disclosure agreement.
To protect yourself and your clients, follow these tips when using artificial intelligence at work: use a corporate or institutional account to access artificial intelligence tools like ChatGPT, rather than your personal account, and ask your company to share its official policies regarding the use of artificial intelligence at work.
Hallucination risks
Because large language models like ChatGPT are essentially word prediction engines, they lack the ability to verify the accuracy of their outputs. This is why artificial intelligence “hallucinations”—invented facts, citations, links, or other material—are an ongoing problem.
You may have heard of the Chicago Sun-Times summer reading list, which included entirely fictional books. Or the dozens of lawyers who submitted legal briefs written by ChatGPT, only to find out the bot invented cases and laws that do not exist.
If you are using artificial intelligence tools to complete projects at work, always make sure to check the output for hallucinations. The only solution to this? Good old-fashioned human review.
Bias risks
Artificial intelligence tools are trained on vast amounts of material—articles, images, artwork, research papers, YouTube transcripts, and more. This means these models often reflect the biases of their creators.
While major artificial intelligence companies try to calibrate their models so they do not make offensive or discriminatory statements, these efforts may not always succeed. For example, when using artificial intelligence to screen job applicants, the tool might filter out candidates from a certain race.
Injection attacks and data poisoning
In prompt injection attacks, bad actors design artificial intelligence training material to manipulate outputs. For example, they can hide commands in metadata and essentially trick large language models into sharing offensive responses.
According to the UK National Cyber Security Centre, “prompt injection attacks are one of the most widely reported vulnerabilities in large language models.”
In data poisoning attacks, a bad actor intentionally “poisons” the training material with bad information to produce undesirable results. In both cases, the result is the same: by manipulating inputs, bad actors can provoke untrustworthy outputs.
User error
Meta recently created a mobile app for its Llama AI tool. The app included a social feed showing questions, texts, and images generated by users. Many users did not realize their chats could be shared this way, leading to embarrassing or private information appearing in the social feed.
This is a relatively harmless example of how user errors can lead to embarrassment, but do not underestimate the potential for user errors to harm your business.
Intellectual property infringement
Are you using artificial intelligence tools to generate images, logos, videos, or audio material? It is possible, and even likely, that the tool you are using was trained on copyrighted intellectual property.
Therefore, you could end up with an image or video that infringes upon an artist’s intellectual property, which could result in a lawsuit filed directly against your company. Copyright law and artificial intelligence are like the Wild West right now, and several massive copyright lawsuits remain unresolved.
Unknown risks
This might sound strange, but with very new technologies, we simply do not know all the potential risks. You may have heard the saying “we don’t know what we don’t know,” and this applies heavily to artificial intelligence.
This is doubly true for large language models, which are somewhat like a black box. Often, even the creators of smart chatbots do not know why they behave the way they do, which makes security risks somewhat unpredictable.
Best practices
To use artificial intelligence safely at work, follow these tips: if possible, use a corporate or institutional account to access artificial intelligence tools, rather than your personal account. Take the time to understand the privacy policies of the smart tools you use.
Do not upload PDFs, images, or texts containing sensitive customer data or intellectual property unless you are authorized to do so. Always review artificial intelligence outputs for hallucinations, errors, and bias.
Conclusion
Artificial intelligence is a powerful tool that can significantly improve productivity, but its use requires caution and an understanding of potential risks. From compliance risks to hallucinations, bias, and intellectual property infringement, there are several challenges that must be addressed. The solution lies in continuous education, following best practices, and maintaining a balance between harnessing the power of artificial intelligence and protecting sensitive data while complying with regulations.
