Generative AI Security Risks: How to Protect Yourself

As anticipated, the piece I wrote about DarkBERT generated a bunch of emails (roine@roineland.com if you’re wondering) about safety on the deep/dark web. I will, of course, oblige and give you a little report about staying safe while dabbling with deep web AI. Or, actually, it’s a good idea to stay vigilant, generally, with AI.

Generative AI is a rapidly growing field with the potential to revolutionize many industries. However, like any new technology, it also comes with some security risks. Here are some of the most common security risks associated with generative AI.

Data poisoning: This is a type of attack where malicious actors intentionally introduce incorrect or misleading data into a generative AI model. This can cause the model to generate incorrect or misleading outputs, which can be used to deceive people or damage systems.

Model theft: Generative AI models are often trained on large amounts of data. This data can be valuable to malicious actors, who could steal it and use it to train their own generative AI models. These models could then be used to generate fake content, attack systems, or commit other crimes.
Deepfakes: Deepfakes are videos or audio recordings that have been manipulated to make it appear as if someone is saying or doing something they never said or did. Generative AI can be used to create deepfakes that are very realistic, making it difficult to distinguish them from real content. Deepfakes can be used to spread misinformation, damage someone’s reputation, or commit fraud.
Malware: Generative AI can be used to create malware that is disguised as legitimate content. This malware can then be used to steal personal data, install ransomware, or commit other malicious activities.
Privacy violations: Generative AI models are often trained on large amounts of personal data. This data could be used by malicious actors to track people’s online activities, identify them in images or videos, or even generate fake content that targets them specifically.
These are just some of the most common security risks associated with generative AI. As the technology continues to develop, it is likely that new risks will emerge. It is important to be aware of these risks and take steps to protect yourself from them.

Here are some tips for protecting yourself from the security risks of generative AI:

Be careful about the data you share: When using generative AI applications, be cautious about the data you share with them. Only share data that you are comfortable with being used to train the model.
Use secure connections: When using generative AI applications, make sure to use secure connections, such as HTTPS. This will help to protect your data from being intercepted by malicious actors.
Keep your software up to date: Make sure to keep your software up to date, including your generative AI applications. This will help to protect you from known security vulnerabilities.
Be aware of the risks: Be aware of the security risks associated with generative AI and take steps to protect yourself from them.
By following these tips, you can help to protect yourself from the security risks of generative AI.

In addition to the above, there are a number of other things that can be done to mitigate the security risks of generative AI. These include:

Using robust security measures to protect data: This includes using encryption, access controls, and other measures to protect data from unauthorized access, use, or disclosure.
Training generative AI models on data that is carefully curated and filtered to remove sensitive or potentially harmful information: This can help to reduce the risk of data poisoning attacks.
Using generative AI models in a controlled environment: This can help to prevent malicious actors from gaining access to the models or the data they are trained on.
Monitoring generative AI models for suspicious activity: This can help to identify and respond to potential security threats.
The security risks of generative AI are a serious concern, but they are not insurmountable. By taking steps to mitigate these risks, we can help to ensure that generative AI is used safely and responsibly.