Data Security in Generative AI: Challenges & Solutions

If you have ever used ChatGPT to find answers to your text queries or DALL-E to generate images, you are already familiar with generative AI. In a nutshell, this type of artificial intelligence is capable of creating unique text, images, and other media as a response to a user query, using training datasets. Despite the advancement of such solutions, they are not without their drawbacks. In particular, one of the key problems with using such solutions is, unsurprisingly, the privacy of user data, which we will actually discuss below.

What Are Some Challenges of Generative AI

So, what are the challenges of generative AI? Let's find out right now.

• Ethics of generative AI models. The ethics of models used by generative artificial intelligence means, first of all, the absence of bias in their responses, as well as ensuring their objectivity and transparency. In practice, providing the first two properties can be difficult – mainly because a service provider developing an AI model usually has limited data samples. As for transparency, it also indirectly depends on the size of the data samples – the more limited and narrowly specialized they are, the greater the risk that the generated solution to the user problem will not consider generally accepted provisions and axioms.
• Using someone else's intellectual property. Generative AI always uses some sample of data for training. This data, in turn, may be protected by copyright – therefore, there may be a difficulty in transferring ownership of the generated content. Actually, this highlights a larger issue that questions the integrity of the use of AI, especially for commercial purposes. If we add to this the blurring of the geographical boundaries of laws related to the generative AI applications, it becomes clear that the solution to this problem must be taken at the international level.
• Potential harm from generated responses. Some organizations in the healthcare, financial, legal, and other sectors, using generative AI, expose their consumers to certain risks – the fact is that the answers it produces may contain potentially dangerous recommendations. Moreover, due to the possible differences in the context of user queries, identifying those answers that can cause harm to users can be quite difficult. That is why, along with the careful development of generative AI models, it is also important for these organizations to pay attention to the implementation of other mechanisms that would set delimitations for the generated answers.
• Compliance with generally accepted user data privacy policies. Finally, if you plan to gradually expand the geographic reach of your AI solution, you will need to ensure that it meets the user data security and privacy standards in the region where the specific user is located. Specifically, these may include the EU General Data Protection Regulation (GDPR), the EU Artificial Intelligence Act (EU AI Act), the California Consumer Privacy Act (CCPA), and so on. But that’s not all: currently, due to the popularization of AI in various fields, a lot of new legislative provisions are being developed, such as the UK’s AI and Data Protection Risk Toolkit, the NIST AI Risk Management Framework, China’s Generative AI Measures, India’s Ethical Guidelines for AI in Healthcare and Biomedical Research, and so on. That’s why long-term fixing user privacy issues in generative AI solutions may be quite challenging.

Solutions for Data Security in Generative AI

In this section, we propose to understand how you can overcome the above-described obstacles in AI data security or, at least, mitigate the consequences of encountering them.

• Creating ethical AI solutions. Data for training models is collected from people who may introduce their own biases. Moreover, AI itself can create biases according to its own algorithms for interpreting the data used for training. Thus, to ensure the transparency of the answers produced by a solution based on generative AI, it is important to additionally implement algorithms for comparing this data with third-party reliable sources of information. 
• Introduction of comprehensive data security AI mechanisms. Any data considered to be someone else's intellectual property and used by generative AI for learning may cause harm or loss to its owners who provide it. To prevent this, you should take care in advance of non-disclosure of your users' information and, as an option, provide them with clear and understandable notifications about for what purpose, by whom, and how their data may be used by your software in the future.
• Eliminating harm from generated responses. To ensure that the responses made by your AI-powered solution are safe for end users, you need to pre-define the rules and parameters that the AI ​​must follow when generating them, as well as implement checking and verification mechanisms to make the generated content safe. And, of course, do not forget to introduce an AI and data security system for monitoring your solution’s operation, which would allow you to fix situations with potentially harmful responses – this will give you a direction for further optimizing your training model. 
• Compliance with AI regulations. The first thing to start with is to ensure reliable user data access control and compliance with the most strict encryption and privacy standards. However, due to the constant discovery of new vulnerabilities even in the most reliable generative AI security mechanisms, you will need to conduct regular checks and audits of the operation of your AI driver solution. Finally, you will need to train the team working on your project on generally accepted rules for using AI and data for its training.

Conclusion

Now, being aware of the most common generative AI security risks, you can start working on your project, keeping it with all necessary policies and standards in time. Also, if you are looking for a service provider to delegate secure AI development without unnecessary doubts, feel free to contact us.