Understanding Generative AI Risks: A Comprehensive Look at the Top 7 GenAI Risks for Businesses

In a world where technological advancements are reshaping industries at a rapid pace, generative AI has emerged as a groundbreaking force. Though still in its nascent stage, organizations across various sectors are fervently exploring the immense possibilities that generative AI fosters. 

While we are still in the early days of the development of generative AI, it is evident that this technology has the potential to revolutionize the way we work and live. Today's blog post will provide further clarity on generative AI, outline the seven risks it presents for businesses, and discuss how to start managing and mitigating these risks. If you prefer learning by watching, we invite you to watch the on-demand webinar that inspired this blog post.

Cutting Through the Noise: What is Generative AI? 

The world of AI is nothing if not jargon-packed. While some commentators use terms interchangeably, not all AI is built the same. Generative AI refers explicitly to a relatively new approach to building advanced neural networks called a "transformer," which can can broadly take any kind of input (text, code, image, video, audio) and produce any kind of output (text, code, image, video, audio). The most commonly used/well known is text-based input to code, text, image, video, or audio output. 

Thus, generative AI generates things, whether that be content, code, or even art.

While there are still many unknowns surrounding the capabilities of these systems, one thing is clear: Generative AI has the potential to radically transform our world, but it brings risk along with it.

What is GenAI Used For? 

Generative AI is a technology still in its early stages of adoption in enterprise workflows. However, the potential for generative AI to transform our work is enormous. GenAI is disrupting industries across healthcare by revolutionizing personalized medicine, finance by enhancing investment strategies, education by facilitating adaptive learning, and transportation by optimizing traffic management systems.

And, the most immediate use case for generative AI experience now is human expert augmentation. By utilizing generative AI tools and systems, human experts can transform into superhumans, capable of working faster and with greater capabilities than ever before.

An example of generative AI's efficacy, or perceived efficacy, can be observed in the recent announcement that IBM will temporarily suspend hiring and instead employ AI to replace 7,500 positions. IBM will rely on generative AI tools, particularly code generation tools, to augment engineers instead of hiring additional people. This approach reduces recruitment expenses and enhances operational efficiency and productivity. 

This example is merely the tip of the iceberg when it comes to workplace possibilities for generative AI. From law to education, creative arts to medicine, almost every industry can conceivably gain and lose from AI augmentation or support. 

As organizations delve deeper into the realm of generative AI, it will become increasingly crucial to foster comprehensive discussions surrounding the economic, legal, and ethical implications of this technology and its impact on the future of work.

Not Everyone is Riding the Wave of GenAI

It's clear that generative AI has enormous potential to transform the way we work. Yet not all organizations are rushing to adopt this technology in their workflows. 

Many businesses are hesitant due to concerns about the risks and challenges associated with implementing generative AI — not to mention the dramatic headlines claiming the end of humanity as we know it!

But organizations are right to be concerned. There are lots of unknowns when it comes to AI technology available today, and businesses should show caution when bringing new risk elements into their systems. 

Fundamentally, the lack of enterprise-ready guardrails to understand and manage risks, built into generative AI systems is a key concern for organizations. 

As the technology is still in its early stages, many organizations are worried about not having the right tools, expertise, or processes to effectively manage and mitigate the risks associated with using generative AI. While businesses know that adopting AI is critical to remain competitive, there is a low-risk appetite for AI technologies which may prove harmful.

Already, we've seen examples of what happens when generative AI gets it wrong. 

Failure modes of these systems, such as hallucinations/confabulations or presenting false information as fact, expose organizations to risks such as data privacy , IP leakage, and operational risk. For businesses to adapt generative AI, these failures must be understood and mitigated.

The 7 Risks of Generative AI: What Organizations Need to Know

Mitigation always begins with identification. That's why, at Credo AI, we have spent significant time studying the risks of the latest AI systems, including GPT-4 by OpenAI and others. 

Drawing from our extensive research and customer interactions, we condensed the seven key risks businesses should be fully aware of when it comes to Generative AI and then listed them for you right here.

Without further ado, let's jump in. 

1. Hallucinations or Confabulations

The term "hallucinations" itself is extensively debated for inappropriate personification — assigning the distinctly human trait of hallucinating to computer systems. Regardless of the name used, the underlying issue remains: generative AI systems can produce information that may seem factual but is not. 

Given that these systems are trained on vast amounts of language data, they can generate convincing sentences based on prediction rather than objective truth. In other words, Generative AI works almost like a guessing game where the system generates the most likely answer based on its training data. 

Ask a generative AI model to give you a link from The Guardian as a reference to support your argument, for instance, and it will create a URL link complete with a relevant title and accurate URL formatting. Paste it into your browser, however, and you'll find that article never existed. The URL is an AI 'hallucination' that has even tricked academics.

Hallucinations can pose a significant risk for organizations relying on these systems for decision-making, content creation, or research. If false or misleading information is presented as fact and clients or employees act on it without knowledge, it can lead to reputational harm and legal liabilities. 

Moreover, these hallucinations can be challenging to spot. AI often gets things right, and not every fact that it churns out is a hallucination. This makes  userscomplacent — there's no need to fact-check every single sentence, is there? The only way that generative AI content can be checked is manually by a human. If (or when) hallucinations make their way into the content we accept as reliable, this can cause serious problems.

✅ For example: A financial institution uses a generative AI system to provide investment recommendations. The system generates a convincing report suggesting a specific stock, but the report is a hallucination based on training data, not on the stock's actual performance. A client invests based on the misleading report and suffers significant financial losses when the stock underperforms. This case could lead to reputational harm, financial losses, legal liabilities, and compliance issues for the financial institution. 

2. Harmful Content

Generative AI systems are essentially input-output models. To 'train' them, AI providers input vast amounts of data from the internet. The AI system then 'reads' this content and generates new content based on patterns and predictions it makes.

The trouble is, if you put bad data in, you'll get bad data out. Unfortunately, the internet contains toxic, dangerous, and harmful content, which AI models consume with no moral filter. As a result, AI can sometimes generate negative content.

Providers of AI models are attempting to mitigate this risk by introducing filters to prevent harmful content from being outputted; the risk of hate speech, profanity, and other damaging content still exists. 

Harmful content is especially concerning for companies considering using a generative AI tool as a customer-facing tool — ask Microsoft, whose Bing chatbot went rogue and professed its love for a user, even going so far as asking him to leave his wife! The risk of any generative AI system outputting offensive or damaging content to customers is high, which can lead to significant brand damage, not to mention the meme potential. 

✅ For example: A company implements a generative AI chatbot on its website. Due to training on diverse internet data and no guardrails, the chatbot generates offensive content in response to a customer's inquiry. The customer shares their negative experience on social media, damaging the company's reputation.

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3. Algorithmic Bias

Due to societal biases inherent in the training data, there is a risk that the generated content will lack representation of the entire community, resulting in prejudicial content that creates real harm to individuals and marginalized communities.

This well-documented risk creates issues for organizations, including biased decision-making, unequal treatment, and restricted resource access. Such consequences can negatively affect the organization's reputation and working environment, potentially leading to legal repercussions.

✅ For example: If a company uses a biased generative AI model to generate job descriptions or to select candidates for job interviews, the model may perpetuate existing gender or racial biases, hiring only men. This will result in a less diverse workforce and may even result in legal action being taken by candidates who unfairly missed out. 

4. Mis-information and Influence Operations

Generative AI tools can be used to create realistic yet fabricated content, enabling the launch of disinformation campaigns and the manipulation of individuals. 

This raises significant concerns for organizations as malicious actors can exploit these capabilities to deceive their way into the organization, potentially leading to security breaches.

Furthermore, these operations pose a risk to society's trust in information, media, and the democratic process, with long-term and far-reaching consequences. Indeed, influence operations have already demonstrated their ability to manipulate public opinion and even sway elections in certain countries.

For organizations, the risks extend beyond data loss and system access, encompassing reputation damage and loss of customer trust if perceived as complicit in these operations. As with any powerful tool, there is always the potential for abuse, and organizations and individuals alike need to take steps to protect themselves and their information.

✅ For example: A bad actor can generate false political information that can be spread to millions of people, causing confusion, eroding trust in institutions, and even leading to violence or other harmful actions.

5. Intellectual Property (IP)

Intellectual property refers to the creations of the mind, such as inventions, literary and artistic works, symbols, names, and images used in commerce. The risks towards IP in generative AI models relate to both the inputs and outputs. 

On the output side, there is a risk that the output generated by the AI tool might violate someone else's intellectual property, exposing the company to potential lawsuits and legal risks. 

On the input side, there is a risk that employees interacting with these systems might cause leakages of their own intellectual property. This is particularly a big risk when using publicly available generative AI tools like ChatGPT, where any information inputted into the system is stored and incorporated into the training data set for the underlying model. 

Lastly, IP poisoning is also an emerging concern, found when organizations that constantly leverage generative AI tools to inform a company's own IP inadvertently inject subtle patterns or trends that reduce competitive advantage over time. 

✅ For example: A company utilizes generative AI to develop a new logo for their brand. However, unbeknownst to them, one of the generated designs closely resembles an existing logo owned by another company. If the company unknowingly uses this logo, they face the costs of a rebrand, the economic loss of a poor logo, and branding effort, not to mention the risk of IP law violations, financial liabilities, and reputation damage. 

6. Privacy 

Privacy in the context of AI refers to the protection of an individual's personal information when interacting with AI systems. This includes both the inputs and outputs of the AI system. The risk associated with privacy concerns in AI is that if personally identifiable information (PII) is mishandled in the input process, it may become part of the training data set and, as a result, expose the organization to compliance risks and legal liability. 

Similarly, there is a risk that the output of the AI system may contain PII, which may also lead to legal and regulatory issues. Companies need to implement appropriate safeguards to prevent PII from being input into these systems and ensure that these systems do not output PII, remaining compliant with data privacy regulations.

✅ For example: A healthcare organization uses generative AI to analyze patient data and provide personalized treatment recommendations. If the AI system mishandles personally identifiable information (PII) during input or outputs PII, the organization risks non-compliance with data privacy regulations, legal liabilities, and reputational damage. 

7. Cybersecurity

Cybersecurity refers to the protection of internet-connected systems, including hardware, software, and data, from theft, damage, or unauthorized access. The risk associated with generative AI and cybersecurity comes in two forms. 

Firstly, the outputs of these systems can introduce security vulnerabilities into the underlying codebase. If the organizations are using the outputs of generative AI tools, particularly the code outputs, they must ensure that the outputs are secure. 

Secondly, bad actors can use these generative AI tools to write sophisticated cybersecurity attacks, phishing attacks, or social engineering attacks more efficiently and effectively. 

As a result, companies must take action to protect their security posture from the various ways in which bad actors can use these systems.

✅ For example: A company integrates generative AI into its software development process. Unfortunately, the AI-generated code contains unnoticed security vulnerabilities. When the code is deployed without proper security measures, it exposes the company's systems to potential theft, damage, or unauthorized access.

8. BONUS: Unknown Risks

While we have discussed the seven known risks of Generative AI, it's important to acknowledge that our understanding of these systems is still incomplete. There are aspects and capabilities that remain unknown to us, creating a vast territory of unexplored risks; the "unknown unknowns." 

It is crucial for businesses and individuals to be well-prepared and continuously test and explore these systems to uncover their emergent capabilities and associated risks. We are in the early stages of implementing this software in real-world scenarios, moving beyond the confines of the laboratory. This transition introduces actual data and real people, opening up a realm of discovery and learning. 

Therefore, there is much more to uncover and comprehend before we can fully grasp the comprehensive range of potential risks and effectively mitigate them, and there are likely to be future risks that we have not yet considered. 

The  Starting Steps to Understand  Generative AI Risks

To confront these challenges head-on, it is essential for organizations first to recognize the transformative nature of generative AI. Regardless of preparedness, this technology is reshaping industries. Assigning individuals within your organization to take accountability for managing these risks becomes vital. Equipping them with the necessary resources and authority will enable robust risk management and mitigation programs tailored explicitly to generative AI tools.

Secondly, establishing clear policies, processes, and a dedicated program is imperative. Such a program should define acceptable uses of generative AI while identifying potential risks and taking proactive measures to address them.

Lastly, employee education plays a critical role. By providing comprehensive training, you empower your workforce to understand the capabilities and limitations of generative AI. This fosters responsible usage and minimizes risks, ensuring your organization utilizes generative AI effectively while maintaining a secure and well-managed environment.

Generative AI Guardrails: Mitigate Risks and Unlock the Benefits of Generative AI

At Credo AI, we understand the importance of managing and mitigating risks associated with generative AI systems. That's why we have developed GenAI Guardrails, a comprehensive offering designed to assist organizations in effectively governing their generative AI systems. 

GenAI Guardrails combines recommendations for policies and processes with technology tools that enable you to implement governance and control layers around your generative AI models. 

With this protection, you can filter out harmful content, insulate your organization from risks, and provide users with guidelines for safe and effective interactions with these systems.

Final Thoughts

Generative AI, for all its faults, is here to stay. While these seven risks prove businesses are right to be cautious about adopting new technologies, the reality is that competitive advantage is only sustainable with constant innovation. 

New AI technologies cannot be ignored, but they can be safely used to minimize business risk and operational disruption.

We believe that the key to the successful adoption of GenAI tools is a strong foundation of policies, processes, and risk management that becomes embedded in company culture. This includes educating your team members on the harms of AI and modeling correct use behaviors. It also includes working with enterprise-level AI systems and choosing an AI governance  partner, such as Credo AI, with a proven track record of managing and mitigating AI  risk to you business.

To learn more about Credo AI's work managing the risks of generative AI, click here. Alternatively, we'd love to discuss how we can help your business get ahead with AI. Reach out to our team directly here.