Managing the Data Security Risks of Generative AI

If you’ve been keeping up with recent online developments, you most likely have heard (more than enough) about the rise of the generative AI chatbot. ChatGPT is the most famous of these chatbots, and it can be used for everything from responding to girlfriends’ texts to writing code for your web application. Handy though generative AI may be, it can also infiltrate your network and access your data if there are insufficient security protocols. 

Clearly, generative AI poses a security risk, but the benefits for users are too substantial to completely rule out using it. Using a chatbot can save time and resources, making it a useful tool for many companies. To make the most of a chatbot, however, data security solutions are essential for minimizing your data security risks and complying with privacy regulations.

Generative AI is Here to Stay

Chatbots have come a long way since they were pioneered in the 1960s, a time when they only responded to basic user inputs. The modern chatbot is a reinforcement-based AI learner that can crawl either the Internet or provided databases for information and then generate long-form writing like articles or blog posts, create innovative solutions for coding problems, and break down complex topics. Chatbots can be very helpful. They reduce the amount of research and initial work needed to start a project, saving users and organizations time and resources. 

At the time of this writing, ChatGPT is one of the most popular chatbots, but other AIs are out there. While ChatGPT has a wide range of applications, it does not connect to the Internet. Bard, in contrast, is Google’s experimental AI chatbot that has access to Google’s search engine and may prove to be more accurate and more current than ChatGPT. Further developments that improve the chatbots will make them an asset to businesses that want to keep up with the breakneck pace of content creation and security weaknesses. That in mind, it’s important for the businesses that use the chatbots to understand the potential risks. 

Generative AI Creates Security and Compliance Challenges

Although ChatGPT does not browse the Internet to collect data for its responses, users access it via the Internet. So, any sensitive data that users put in ChatGPT or any other generative AI is at risk of exposure. Additionally, generative AI that does webcrawl, like Bard and its equivalents, may find sensitive data not intended for unencrypted software to access. Companies that don’t know where all of their data are stored or have poor security may find that the chatbots are able to access and use that data to formulate responses. 

Alternatively, if an attacker uses an insecure network connection or malware to view the messages you type in the chatbot, it is a short jump to uncovering proprietary information or code. Any personal information given to a generative AI will contribute to its learning, which means that if you put in a part of your company’s code, personally identifying information, or private data, you risk exposing it to bad actors and violating compliance regulations. 11% of the content posted in ChatGPT by employees was reportedly sensitive company information, which violates GDPR and CCPA standards and could result in fines.  

The chatbots are not foolproof. For example, if you ask a chatbot to create code, it’s necessary to check it for vulnerabilities. This is often overlooked due to confidence in AI, but if you don’t check, it’s very possible that an attacker will be able to identify and exploit any vulnerabilities in your chatbot-generated code. As a result, your data could be exposed.

Securing Corporate Data Against Generative AI

Evidently, many of the issues with generative AI are a result of human error. Users enter sensitive data or do not adequately secure their systems, which creates vulnerabilities and potential vectors for attack. Training for your employees is an essential part of improving your security. Employees need to have a good grasp of what data can be shared with generative AI, and they need to be aware that they must proofread or test its responses before implementing them. Without training, employees are more likely to unintentionally compromise your organization’s data.

Training is essential to address the issues with using generative AI, but there are other strategies you can use to protect your data. Limit employee access to data so that they can only access that data which they need to do their jobs. Focus on data organization and classification, and implement data protection measures like encryption. You can also use data masking, which hides the values of data you put in to reduce the risk of someone seeing private information.

While generative AI can help your organization to maximize its time and resources, remember that chatbots are not designed to worry about your data security for you. You have to protect yourself from incorrect or outdated information and flawed code, webcrawling AIs, and attackers who steal the information you put into the chatbots. Always assume the chatbot poses a security risk, and take appropriate security precautions to combat that risk. If you are prepared, you can reduce your risk of attack as well as minimize the damage in the event of a successful data breach.

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