Generative AI
Generative AI is an artificial intelligence technology that utilizes large datasets of billions of pages of information to generate tailored responses to user queries or requests. The answers produced by these systems are highly analytical and comprehensive, as they are developed using multiple sources such as books, articles, and other textual resources. The emergence of Generative Adversarial Networks (GANs), a subset of machine learning algorithms, has led to the increased prominence of Generative AI. As a result of the wide availability of free-to-use Generative AI models in the market, it has become easily accessible to the general public. While Generative AI has numerous beneficial applications, cybercriminals have already begun to use it to conduct cyber attacks.
A comic strip on deep fakes
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Why must employees be aware of Generative AI cyber security risks?
Generative AI can impact employees and your organization if they are unaware of its risks. Generative AI can create deep fakes and cause various cyber attacks that can be difficult to detect, potentially leading to data breaches, financial loss, or reputational damage to the organization. Employees can become better equipped to identify potential security risks and biases associated with these systems by providing cybersecurity awareness training on Generative AI. This training can help prevent cyber attacks caused by Generative AI and protect the organization’s data and systems.
FAQs
Get answers to essential questions on Generative AI and Cyber Security.
Generative AI, a part of machine learning, aims to generate new, original data, such as images, text, or sounds, rather than simply predicting or analyzing existing data. This is achieved through specialized algorithms and models that can either be instructed to produce specific outputs based on user inputs or trained to learn from large datasets and generate new, previously unseen data.
Generative AI, such as natural language processing models like ChatGPT, transforms various industries by simplifying complex tasks. For instance, researchers can leverage generative AI to extract information quickly and efficiently, which would otherwise take hours of manual labour. Generative AI generates unique content by utilizing vast amounts of data from datasets, helping researchers discover new insights and knowledge in their field.
One of the key limitations of generative AI is the potential for biased or inappropriate content to be generated due to the need for more control over the output. Another limitation is the risk of developing plagiarized content, which could lead to copyright infringement if incorporated into an article without proper attribution. Additionally, obtaining sufficient training data can be a significant challenge in specific domains, and preprocessing may be required to ensure the data is suitable for training the model.
There have been both positive and negative impacts of generative AI on cybersecurity. Generated AI is capable of detecting threats, assessing vulnerabilities, and detecting malware. However, attackers can also use generative AI to create and launch adversarial attacks that can bypass security systems. For example, they can generate fake identities, phishing emails, or deep fakes that can deceive users and evade detection by security systems.
- Threat actors can generate hyper-personalized phishing emails
- Chatbot poisoning – Threat actors could poison the chatbots using generative AI and initiate cyber attacks.
- Copyright infringement – If the user copy and paste a code or content directly it can lead to copyright violations.
- If sensitive information is shared it can lead to data breach or compromise
