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Generative AI Security: 8 Risks That You Should Know

Generative AI Security: 8 Risks That You Should Know

AI is all around us. Every day, thousands of new AI programs like ChatGPT, Generative AI, and others pop up. We always hear that if we don't use AI, we'll be left behind. But don't let the urge to use AI make you forget about the online safety risks.

We are putting AI in our web browsers, emails, and file systems. We let it work for us like a digital helper, even sharing our own and our business data with it. All this can lead to new online safety problems and increase the chances of old-fashioned hacking.

Generative AI has brought big changes to many areas, like art and content creation. But as more people use this technology, we need to think about keeping data private and secure. In this blog post, we're going to look at how Generative AI affects data security and how we can mitigate any possible risks.

The Rise of Generative AI & How does it work?

Generative AI is a type of machine learning program that operates through generative modelling. This technique trains models to generate new data that share the same patterns and characteristics as the training data. Here's how generative AI works:

  • Data Collection: Generative AI needs a substantial amount of data to train on. This could be anything - pictures, text, music, etc., depending on what the AI is being trained to generate.
  • Training: Once the data is collected, a specific type of neural network called a Generative Adversarial Network (GAN) is often used to train the model. GAN consists of two parts: a generator and a discriminator. The generator creates new data instances, while the discriminator evaluates them for authenticity; i.e., it decides whether each instance of data belongs to the original dataset.
  • Learning: The generator and discriminator are then set against each other. The generator tries to create the best possible imitation of the data, and the discriminator tries to get better at figuring out which data is real and which is fake. Over time, the generator gets better and better at making data that looks like the training data, until the discriminator is unable to tell the difference.
  • Generating New Data: Once trained, the generator can create new data instances that are similar to the training data but not identical. This is useful for a variety of applications, like creating new images, writing text, composing music, etc.

Keep in mind that while GANs are a popular method, there are also other techniques used in generative AI, like Variational Autoencoders (VAEs) and autoregressive models. While Generative AI has made some amazing progress, it also brings up problems with keeping data private and confidential.

8 Security Risks of Generative AI

Here are the eight risks of generative AI that you should be aware of.

1.    Data Overflow

People can enter any type of data into Generative AI services via open text boxes, including sensitive, private or proprietary information. Take code-generating services like GitHub Copilot as an example. The code sent to this service could contain not just a company's confidential intellectual property, but also have sensitive data like API keys that have special access to customer information.

2.    IP Leak

Another serious concern is IP leakage and confidentiality when using generative AI, adding that the ease of use of web- or app-based AI tools risks creating another form of shadow IT. Given that these online generative AI apps send and process data over the internet, using a VPN can provide an extra layer of security, masking your IP address and encrypting the data in transit.

3.    Data Training

Generative AI models need a ton of data to learn from, and some of this data might be sensitive. If not managed carefully, this data could unintentionally be revealed during training, causing potential privacy issues.

4.    Data Storage

Generative AI models get better with more data, and this data needs to be kept somewhere while the models learn and improve. This means sensitive business data is kept in third-party storage spaces, where it might be misused or leaked if it's not properly protected with things like encryption and access controls. Organizations should implement a thorough, secure data strategy to prevent breaches.

5.    Compliance

Sensitive data is sent to third-party AI providers, like OpenAI. If this data includes Personally Identifiable Information (PII), it could create compliance problems with laws like the General Data Protection Regulation (GDPR) or the California Privacy Rights Act (CPRA).

6.    Synthetic Data

Generative AI can create synthetic data that looks a lot like real data, which can lead to concerns about people being able to figure out who the data came from. Synthetic data could have small patterns or details that might lead to people or sensitive features being identified.

7.    Accidental Leaks

Generative models, especially ones based on text or images, can unintentionally include information from the training data that shouldn't have been revealed. This could be personal information or confidential business data.

8.    AI Misuse and Malicious Attacks

Generative AI could potentially be misused by malicious actors to create deepfakes or generate misleading information, contributing to the spread of fake news and disinformation. Moreover, if AI systems are not adequately secured, they could become a target for cyberattacks, creating additional data security concerns.


Generative AI offers a lot of possibilities for new ideas and progress in many areas, but we must be mindful of the security problems it can bring. This can be due to the uncensored prompts, accidentally revealing sensitive data, problems with storing data, difficulties with global laws, and information leaks.

Even though there are many concerns linked to generative AI, companies should not dismiss this technology. Rather, they should develop a company-wide plan focusing on building trust in AI, managing risks, and securing AI systems.

This way, organizations can take advantage of what generative AI has to offer while minimizing any potential problems. As we move towards a future filled with AI, making sure we use generative AI securely isn't just a nice thing to have - it's absolutely necessary.

Note: This blog article was written by a guest contributor for the purpose of offering a wider variety of content for our readers. The opinions expressed in this guest author article are solely those of the contributor and do not necessarily reflect those of GlobalSign.

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