In recent years, Generative Artificial Intelligence has emerged as a powerful tool for creating autonomous content, including images, videos and even text.   

With its rapid evolution, this technology presents a series of benefits and challenges for the companies that use it. According to Ben Goertzel, the mathematician who coined the term "generative AI", its rise has the potential to make up to 80% of manual jobs obsolete, representing a real transformation in the global market. 

In the specific context of cybersecurity, Generative AI can be both an ally and a threat, as cyberattacks become more sophisticated and difficult to detect.  

Last month, Meta, the parent company of Facebook, Instagram and WhatsApp, sent out a warning about hackers taking advantage of the high level of interest in new AI tools, such as ChatGPT, to get users to install malicious code on their devices. 

In this article, learn about the key aspects of this interaction between Generative Artificial Intelligence and Information Security, its implications and associated risks. 

Generative AI: understand what it is and recent advances

Generative AI is a subfield of Artificial Intelligence that creates systems to generate autonomous content.  

In recent years, there have been significant advances in the creation of images, texts and videos, especially in models based on machine learning, such as Generative Adversarial Networks (GANs) and Transformers. 

GANs are neural networks made up of two models: a generator and a discriminator, which work together to generate content. They have been used for images, videos and even music. 

Transformers, meanwhile, are machine learning models that have proven to be very effective in creating autonomous text, especially in tasks such as translation and generating answers to questions. 

These advances make it possible to produce increasingly sophisticated and realistic content. However, certain precautions must be taken, especially with regard to cybersecurity. 

Understand how cybercriminals can take advantage of Generative AI to create more sophisticated attacks 

Cybercriminals can take advantage of Generative AI to create more sophisticated attacks in a variety of ways.  

For example, they can use Generative AI to create more convincing phishing attacks, which fool users by appearing more legitimate.  

They can also use Generative AI to create deepfakes, which are fake videos or images that look authentic, to spread false information or defame individuals. 

However, some guidelines for tools such as ChatGPT prevent cybercriminals from addressing certain topics, leaving room for the creation of malicious variants.  

Recently, ISH's Threat Intelligence team, Heimdall, warned of three examples, known as WormGPT, FraudGPT and WolfGPT, which are illegally marketed on Dark Web forums through a subscription model. 

Information security challenges and concerns in relation to Generative AI 

With increasingly intelligent systems, Generative AI presents a number of information security challenges and concerns. Among the main ones, we highlight the following: 

Scalability 

Generative AI can create content on a large scale and at high speed. This makes it difficult to detect malicious content. 

Bias 

Generative AI can be influenced by prejudices and stereotypes present in the training data. It is therefore possible for AI to create discriminatory or offensive content. 

Privacy 

Generative resources can be used to create content that violates people's privacy. This is the case with deepfakes, in which cybercriminals use images or videos of individuals without their consent. 

Regulation 

As it is a relatively new technology, there are still no specific regulations for the use of Generative AI. This can make it difficult to define responsibilities and legal actions in the event of malicious use. 

In view of these risks, it is important for cybersecurity professionals and regulatory authorities to be aware of these issues, working on solutions to mitigate the risks associated with the use of Generative AI. 

How to protect yourself? Strategies and solutions to mitigate risks 

There are a number of strategies and solutions to mitigate the risks associated with the use of Generative AI in cybersecurity. See the main ones below: 

Training detection systems 

It is important that malicious content detection systems are trained to recognize deepfakes and other content generated by Generative AI.  

This may involve using machine learning techniques to identify common patterns and characteristics in malicious content. 

Keeping systems up to date 

Cyber security systems must be updated regularly to ensure that they are protected against the latest threats. 

Invest in authentication technologies 

Authenticating users and devices can help mitigate the risks of social engineering and phishing attacks. The aim of the practice is to prevent cybercriminals from accessing confidential systems and data. 

Educating users 

Users should be educated about the risks associated with using Generative AI. In companies, for example, it is important to adopt policies so that users can identify risks and patterns and avoid falling for phishing or social engineering scams. 

Ethical use of Generative AIs is possible 

As we have seen, there are various strategies and solutions that can be implemented to mitigate the risks associated with the use of Generative AI in cybersecurity.  

It is therefore crucial that companies, cybersecurity professionals and regulatory authorities work together to promote the ethical use of technology, as well as mitigate risks. 

At ISH, we are constantly on the lookout for new cyber threats, seeking to keep companies one step ahead of the criminals. Our cyber security experts are committed to providing effective and innovative solutions to protect our clients' businesses 

Contact us and find out how we work to continually promote a more ethical and secure world for everyone, and how we can help your business stay protected in a reality of constantly evolving threats.