Exploring the Risks of Generative AI

Safeguarding against Copyright, intellectual Property, deception, and disinformation.

About this article

It feels strange to write an article with Generative AI as a co-writer. It feels even stranger to task it with the mission of analyzing and describing the risks of using it. It feels so evil, similar to using someone’s religious confession to convict them for heresy and burning them at the stake.

It’s also a good case study to truly understand the risks of using GenAI for commercial use.

About Generative AI

Generative AI is a field of artificial intelligence focused on creating systems that can generate new data, images, or text that is similar to the examples they were trained on. Generative models have been around for decades, but recent advances in deep learning and neural networks have led to significant improvements in their ability to create realistic and complex outputs.

Past:

One of the earliest examples of generative AI is the Markov chain, developed by Russian mathematician Andrey Markov in the early 1900s. Markov chains are probabilistic models that generate sequences of events based on the probabilities of each event occurring, given the preceding event. These models are widely used in natural language processing, speech recognition, and other applications.

In the 1950s and 1960s, computer scientist John McCarthy developed the concept of generative grammars, which are rule-based systems for generating sentences in natural language. Generative grammars have been used to create chatbots and other conversational agents.

In the 1990s, the first generative adversarial networks (GANs) were developed by Ian Goodfellow and his colleagues at the University of Montreal. GANs consist of two neural networks: a generator network that creates new data, and a discriminator network that evaluates whether the generated data is realistic or not. GANs have been used to create realistic images, videos, and even music.

Present:

Today, generative AI is a rapidly advancing field with a wide range of applications. One of the most popular models is the Transformer, which was introduced by Google in 2017. Transformers are a type of neural network that can process sequences of data, such as text or speech, and generate new sequences that are similar to the input.

Generative models are also being used for a wide range of applications, including natural language processing, image and video generation, and even drug discovery. In recent years, there have been several notable advances in generative AI, including the introduction of GPT-3 by OpenAI, which is capable of generating highly realistic and complex natural language outputs.

Overall, generative AI has come a long way since the early days of Markov chains and generative grammars. Today’s generative models are capable of creating highly realistic and complex outputs, and are being used for a wide range of applications in fields ranging from art and entertainment to healthcare and science.

GenAI, copyright and intellectual property

One of the primary risks of using generative AI is the potential infringement of copyright and intellectual property rights. This is because generative AI models can be trained on existing copyrighted or trademarked materials, such as text, images, and music, to create new and original content that may infringe on these rights.

There are several ways in which generative AI can pose a risk to copyright and intellectual property. For example:

Unauthorized use of copyrighted materials: If a generative AI model is trained on copyrighted materials without permission from the copyright owner, any content generated by the model could potentially infringe on the owner’s rights.
Infringement of trademarks: If a generative AI model creates content that includes trademarked logos, slogans, or other protected intellectual property, it could potentially infringe on the owner’s rights.
Creation of derivative works: Generative AI models can create new and original works based on existing materials. However, if these works are too similar to the original materials, they could be considered derivative works, which may infringe on the owner’s rights.

To mitigate these risks, it is important to ensure that the generative AI model is trained on legally obtained and properly licensed materials. Additionally, it is essential to have a thorough understanding of copyright and intellectual property laws and to seek legal advice before using generative AI for any commercial or creative purposes.

GenAI, bias and errors

Generative AI has the potential to produce biased or erroneous output, which can have serious consequences for enterprises. The following are some of the risks associated with using generative AI that are related to bias and errors:

Bias in the training data: Generative AI algorithms are trained on large datasets, and if these datasets are biased, the AI may produce biased output. For example, if a chatbot is trained on a dataset that contains language that is discriminatory or offensive, the chatbot may produce similar language in its output.
Overfitting: Generative AI algorithms can be susceptible to overfitting, which occurs when the AI is trained too closely on a specific dataset, leading to overconfidence in the AI’s ability to generalize to new situations. This can result in output that is too rigid and does not adapt well to new data.
Lack of diversity: If the training data used for the generative AI is not diverse, the output may not be representative of the full range of possible outputs, leading to errors and bias.
Inaccurate or incomplete data: If the input data used to train the generative AI is inaccurate or incomplete, the output may be unreliable or inaccurate.

To mitigate these risks, enterprises should take the following steps:

Use diverse and representative training data: Enterprises should use datasets that are diverse and representative of the target audience to reduce the risk of bias. This can help ensure that the AI produces output that is fair and unbiased.
Regularly update the training data: Enterprises should regularly update the training data to ensure that the AI is exposed to new and diverse data, reducing the risk of overfitting.
Monitor the output: Enterprises should monitor the output generated by the generative AI to identify potential errors or bias. This can involve testing the AI on new datasets and comparing the output to expected results.
Have a plan for handling errors: Enterprises should have a plan in place for how to handle errors and mistakes generated by the generative AI. This may involve identifying potential errors before they occur, and having a process in place for correcting them.

GenAI and explainability

Generative AI is often considered a “black box” technology, meaning that it can be difficult to understand how the AI arrives at its output. This lack of explainability can pose significant risks for enterprises, particularly in industries where transparency is critical. The following are some of the risks associated with using generative AI that are related to explainability:

Lack of transparency: Generative AI can be difficult to interpret and understand, making it challenging to determine how the AI arrived at a particular output. This can make it difficult to identify errors or bias in the output and can lead to challenges in explaining the AI’s decisions.
Lack of accountability: Without transparency, it can be difficult to hold the AI accountable for its decisions. This can be particularly problematic in industries such as finance or healthcare, where accountability is critical.
Difficulty in compliance: In regulated industries, it may be necessary to demonstrate that AI systems are compliant with regulations. However, without transparency, it can be difficult to demonstrate compliance, potentially leading to regulatory challenges.

To mitigate these risks, enterprises should take the following steps:

Use explainable AI techniques: Explainable AI techniques are designed to make the output of the AI more interpretable and understandable. By using techniques such as model interpretation or feature importance analysis, enterprises can better understand how the AI is arriving at its output.
Use human oversight: In some cases, it may be necessary to use human oversight to review the output of the AI and ensure that it is accurate and unbiased. This can involve having human experts review the AI’s decisions or having a process in place for reviewing and correcting errors.
Document the AI’s decisions: To improve transparency, it may be necessary to document the AI’s decisions and the factors that influenced those decisions. This can help stakeholders better understand how the AI arrived at its output and can facilitate compliance efforts.

GenAI, malicious code, deepfake and malware

Generative AI can be used for both good and bad purposes, and there are risks associated with the misuse of this technology. Misuse of generative AI can take many forms, including the creation of malicious code, deepfakes, and malware. The following are some of the risks associated with using generative AI that are related to misuse:

Malicious code: Generative AI can be used to generate malicious code, such as malware or viruses. Malicious actors can use generative AI to create code that is difficult to detect and can evade traditional cybersecurity measures.
Deepfakes: Generative AI can be used to create convincing deepfakes, which are manipulated videos or images that appear to be real. Deepfakes can be used to spread disinformation or to defame individuals or organizations.
Malware: Generative AI can be used to create new and sophisticated forms of malware that can be difficult to detect and defend against. Malware created using generative AI can be highly targeted, making it more effective at compromising systems and stealing sensitive data.

To mitigate these risks, enterprises should take the following steps:

Use ethical AI practices: Enterprises should ensure that they are using generative AI in an ethical and responsible manner. This may involve establishing guidelines for the use of generative AI and providing training for employees on how to use the technology safely.
Monitor for misuse: Enterprises should monitor for potential misuse of generative AI, such as the creation of malicious code or deepfakes. This can involve using cybersecurity measures such as intrusion detection systems and malware scanners.
Invest in cybersecurity: Enterprises should invest in cybersecurity measures to defend against the misuse of generative AI. This may involve using advanced cybersecurity technologies such as artificial intelligence-based threat detection and response systems.

Overall, while generative AI can be a powerful tool for enterprises, there are risks associated with the misuse of this technology. By using ethical AI practices, monitoring for potential misuse, and investing in cybersecurity measures, enterprises can use generative AI in a safe and responsible manner while minimizing the potential for malicious activities.

GenAI and privacy

Generative AI can pose significant risks to privacy, particularly when it is used to generate or manipulate personal data. The following are some of the risks associated with using generative AI that are related to privacy:

Data breaches: Generative AI can be used to generate synthetic data that is similar to real data, which can be used to train machine learning models without exposing sensitive personal data. However, if synthetic data is not generated correctly, it can reveal sensitive information about individuals, potentially leading to data breaches.
Re-identification attacks: Generative AI can be used to re-identify individuals in anonymized datasets. This can be used to link individuals to sensitive information, potentially leading to a breach of privacy.
Discrimination: Generative AI can be used to create biased or discriminatory models, which can result in the discrimination of individuals based on sensitive information, such as race, gender, or age.

To mitigate these risks, enterprises should take the following steps:

Use privacy-preserving techniques: To protect sensitive personal data, enterprises should use privacy-preserving techniques such as differential privacy, which adds noise to data to prevent individual identification, and federated learning, which trains machine learning models on decentralized data.
Monitor for re-identification attacks: Enterprises should monitor for potential re-identification attacks, such as when an individual can be linked to sensitive information using generative AI. This can involve using techniques such as k-anonymity, which anonymizes data by ensuring that each record cannot be linked to an individual.
Ensure fairness and non-discrimination: Enterprises should ensure that the models generated using generative AI are fair and non-discriminatory. This can involve using techniques such as adversarial debiasing, which trains models to reduce bias in data.

Generative AI can pose significant risks to privacy, particularly when it is used to generate or manipulate personal data. By using privacy-preserving techniques, monitoring for re-identification attacks, and ensuring fairness and non-discrimination in models, enterprises can use generative AI in a safe and responsible manner while minimizing the potential for privacy breaches.

GenAI, deception and disinformation

Generative AI can also pose risks related to deception and disinformation. This is because these models can be used to generate highly realistic and convincing content, such as text, images, and videos, that can be used to deceive or spread false information. Some of the specific risks related to deception and disinformation include:

Deepfakes: Generative AI can be used to create highly realistic videos and images of people saying or doing things that they never actually did. These types of manipulated media, known as deepfakes, can be used to spread false information and deceive people.
Fake news: Generative AI can be used to create highly convincing articles or stories that are completely fabricated. These articles can be shared on social media or other online platforms, leading to the spread of false information and the potential for harm.
Spam and phishing: Generative AI can be used to create highly convincing spam or phishing emails, which can be used to trick people into clicking on malicious links or sharing sensitive information.

To mitigate these risks, it is important to be aware of the potential for deception and disinformation when using generative AI. It is important to verify the authenticity of any content generated by these models and to be cautious about sharing or believing information that cannot be verified through credible sources. Additionally, it may be necessary to develop new tools and techniques for detecting and mitigating the risks posed by generative AI.

Summary table - The risks of using GenAI

Category	Risk	Description
Copyright and intellectual property	Unauthorized use of copyrighted materials	If a generative AI model is trained on copyrighted materials without permission from the copyright owner, any content generated by the model could potentially infringe on the owner's rights.
	Infringement of trademarks	If a generative AI model creates content that includes trademarked logos, slogans, or other protected intellectual property, it could potentially infringe on the owner's rights.
	Creation of derivative works	Generative AI models can create new and original works based on existing materials. However, if these works are too similar to the original materials, they could be considered derivative works, which may infringe on the owner's rights.
Bias and errors	Bias in the training data	Generative AI algorithms are trained on large datasets, and if these datasets are biased, the AI may produce biased output. For example, if a chatbot is trained on a dataset that contains language that is discriminatory or offensive, the chatbot may produce similar language in its output.
	Overfitting	Generative AI algorithms can be susceptible to overfitting, which occurs when the AI is trained too closely on a specific dataset, leading to overconfidence in the AI's ability to generalize to new situations. This can result in output that is too rigid and does not adapt well to new data.
	Lack of diversity	If the training data used for the generative AI is not diverse, the output may not be representative of the full range of possible outputs, leading to errors and bias.
	Inaccurate or incomplete data	If the input data used to train the generative AI is inaccurate or incomplete, the output may be unreliable or inaccurate.
Explainability	Lack of transparency	Generative AI can be difficult to interpret and understand, making it challenging to determine how the AI arrived at a particular output. This can make it difficult to identify errors or bias in the output and can lead to challenges in explaining the AI's decisions.
	Lack of accountability	It can be difficult to hold the AI accountable for its decisions. This can be particularly problematic in industries such as finance or healthcare, where accountability is critical.
	Difficulty in compliance	In regulated industries, it may be necessary to demonstrate that AI systems are compliant with regulations. However, without transparency, it can be difficult to demonstrate compliance, potentially leading to regulatory challenges.
Misuse like malicious code, deepfake and malware	Malicious code	Generative AI can be used to generate malicious code, such as malware or viruses. Malicious actors can use generative AI to create code that is difficult to detect and can evade traditional cybersecurity measures.
	Deepfakes	Generative AI can be used to create convincing deepfakes, which are manipulated videos or images that appear to be real. Deepfakes can be used to spread disinformation or to defame individuals or organizations.
	Malware	Generative AI can be used to create new and sophisticated forms of malware that can be difficult to detect and defend against. Malware created using generative AI can be highly targeted, making it more effective at compromising systems and stealing sensitive data.
Privacy	Data breaches	Generative AI can be used to generate synthetic data that is similar to real data, which can be used to train machine learning models without exposing sensitive personal data. However, if synthetic data is not generated correctly, it can reveal sensitive information about individuals, potentially leading to data breaches.
	Re-identification attacks	Generative AI can be used to re-identify individuals in anonymized datasets. This can be used to link individuals to sensitive information, potentially leading to a breach of privacy.
	Discrimination	Generative AI can be used to create biased or discriminatory models, which can result in the discrimination of individuals based on sensitive information, such as race, gender, or age.
Deception and disinformation	Deepfakes	Generative AI can be used to create highly realistic videos and images of people saying or doing things that they never actually did. These types of manipulated media, known as deepfakes, can be used to spread false information and deceive people.
	Fake news	Generative AI can be used to create highly convincing articles or stories that are completely fabricated. These articles can be shared on social media or other online platforms, leading to the spread of false information and the potential for harm.
	Spam and phishing	Generative AI can be used to create highly convincing spam or phishing emails, which can be used to trick people into clicking on malicious links or sharing sensitive information.

13 rules of thumb on mitigating GenAI risks

Use diverse and representative training data: Enterprises should use datasets that are diverse and representative of the target audience to reduce the risk of bias. This can help ensure that the AI produces output that is fair and unbiased.
Regularly update the training data: Enterprises should regularly update the training data to ensure that the AI is exposed to new and diverse data, reducing the risk of overfitting.
Monitor the output: Enterprises should monitor the output generated by the generative AI to identify potential errors or bias. This can involve testing the AI on new datasets and comparing the output to expected results.
Have a plan for handling errors: Enterprises should have a plan in place for how to handle errors and mistakes generated by the generative AI. This may involve identifying potential errors before they occur, and having a process in place for correcting them.
Use explainable AI techniques: Explainable AI techniques are designed to make the output of the AI more interpretable and understandable. By using techniques such as model interpretation or feature importance analysis, enterprises can better understand how the AI is arriving at its output.
Use human oversight: In some cases, it may be necessary to use human oversight to review the output of the AI and ensure that it is accurate and unbiased. This can involve having human experts review the AI’s decisions or having a process in place for reviewing and correcting errors.
Document the AI’s decisions: To improve transparency, it may be necessary to document the AI’s decisions and the factors that influenced those decisions. This can help stakeholders better understand how the AI arrived at its output and can facilitate compliance efforts.
Use ethical AI practices: Enterprises should ensure that they are using generative AI in an ethical and responsible manner. This may involve establishing guidelines for the use of generative AI and providing training for employees on how to use the technology safely.
Monitor for misuse: Enterprises should monitor for potential misuse of generative AI, such as the creation of malicious code or deepfakes. This can involve using cybersecurity measures such as intrusion detection systems and malware scanners.
Invest in cybersecurity: Enterprises should invest in cybersecurity measures to defend against the misuse of generative AI. This may involve using advanced cybersecurity technologies such as artificial intelligence-based threat detection and response systems.
Use privacy-preserving techniques: To protect sensitive personal data, enterprises should use privacy-preserving techniques such as differential privacy, which adds noise to data to prevent individual identification, and federated learning, which trains machine learning models on decentralized data.
Monitor for re-identification attacks: Enterprises should monitor for potential re-identification attacks, such as when an individual can be linked to sensitive information using generative AI. This can involve using techniques such as k-anonymity, which anonymizes data by ensuring that each record cannot be linked to an individual.
Ensure fairness and non-discrimination: Enterprises should ensure that the models generated using generative AI are fair and non-discriminatory. This can involve using techniques such as adversarial debiasing, which trains models to reduce bias in data.

About the writers

Ron Porat is a seasoned entrepreneur with a wealth of experience in management, technology, and sales. Throughout his career, Ron has founded and led several successful startups, including Hacktics (which was sold to Ernst & Young), Seeker (which was sold to Synopsys), Shine (a network-level ad-blocking solution), and L1ght (a machine learning company dedicated to protecting children). With his vast knowledge and experience, Ron is passionate about helping other entrepreneurs achieve success.

Doron Habsuhsh is the founder and CEO of D.H. Consulting, a boutique consulting company that specializes in mentoring entrepreneurs on how to incubate their ideas into successful businesses. With years of experience and field experience, Doron has co-founded, headed and consulted numerous startups in various fields including technology, business development and marketing.

ChatGPT is a state-of-the-art language model developed by OpenAI that uses deep learning algorithms to generate human-like responses to user queries. It is designed to understand natural language and provide accurate and relevant information on a wide range of topics. ChatGPT is trained on a vast amount of data, including books, articles, and websites, making it a powerful tool for accessing and synthesizing knowledge. With its ability to learn from context and generate diverse and creative responses, ChatGPT is revolutionizing the way we interact with machines and enhancing our ability to access information in real-time.