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Generative AI Data Security Fundamentals

Generative AI Data Security Fundamentals
Generative AI Data Security Fundamentals

The era of generative AI is taking over the digital landscape by a storm. Unlike traditional AI, generative AI models construct new content, designs, synthetic data or deep fakes (digitally forged images or videos) by learning from a substantial number of datasets. Generative AI models can accomplish this with the advent of transformers. Transformers are a type of machine learning that aids researchers to train large models without labeling any of the data in advance. Another feature that transformers have unleashed is ‘attention’, which allows models to track the relations between datasets. This allows for a bulk of data to be analyzed, which generative AI models use to create data.

Although generative AI models have helped in creating a rapid growth trajectory, comparable to that of the internet and the smartphone. There are considerable budding risks, which users of generative AI should be mindful of, including:

  • Data Breaches: Since generative AI models use numerous datasets to familiarize themselves with the creation of content, this could lead to data breaches and security risks if said data is exposed or leaked to the wrong channels.

  • Plagiarism: Generative AI models do not bookmark the information they analyze for creation of content. This could lead to plagiarism of material that is otherwise copyrighted without ways to trace the original source. Along with copyright infringements, another facet of concern would be its generation of fake news.

  • Engineered Cyber Attacks: The creation of synthetic data could be used to launch refined cyber attacks or create realistic deepfakes. These attack vectors could be used for foul reasons to launch cyber attacks on unsuspecting people, tricking them into thinking phishing attack email links are legitimate.

  • Inappropriate or Biased Content: If the generative AI models are using datasets or information that is prejudiced or discriminatory, it could lead to creation of synthetic data that corresponds to the same biases.

  • Data Privacy Violations: The content created by generative AI models might not adhere to legal and ethical regulations which are mandatory principles of data privacy and security. Moreover, the input of private and protected information into the chatbot could lead to violations of data privacy and compliance regulations.

Here are a few practices to moderate the risks:

  • Observe Model Outputs: While the use of generative AI models offers ease, it is essential to observe and review the primary sources it uses while creating data to authenticate information it is using as its base.

  • Model Training: Models should be trained in reliable environments which are separated from the other data sources of the organization, to prevent data breaches.

  • Security Controls: Organizations should implement the best security practices to ensure their data is not put at risk. This could involve (but not be limited to) firewalls, intrusion detection systems, and access controls.

  • Awareness Programs: Awareness campaigns and programs should be organized to help employees be aware of the risks and proposed solutions in case of data breaches. This would alleviate potential risks and help organizations to be better prepared.

  • Anonymizing Data: The data should be anonymized prior to the use of datasets by the models. This would help safeguard sensitive data from synthetic data related cyber attacks and data breaches.

We’re at a threshold where we have only utilized a fraction of what AI could be capable of. However, with its meteoric growth, it is essential for organizations to be transparent about the use of generative AI models, wherever applicable. Generative AI has created a revolutionary leap in innovation of how we use technology, but it is imperative to understand the need to mitigate the risks that come with it.

Generative AI Data Security Fundamentals

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