The innovation of Artificial Intelligence (AI) continues as we move into 2024, with more and more businesses incorporating these technologies into their business operations. Within the realm of AI exists a newer type of groundbreaking technology that is growing more popular by the day — Generative AI, or GenAI. GenAI possesses the remarkable ability to create content autonomously, whether it's images, text, music, etc. However, amidst its great capabilities lies the concerns around safety and security.
Generative AI can pose several risks including deepfakes and data privacy, especially if a company is not prepared and informed about mitigating these threats. According to a Gartner survey, 34% of organizations are either already using or implementing AI application security tools to mitigate the accompanying risks of GenAI, and 56% of respondents said they are also exploring such solutions. Keep reading to learn more about the risks associated with GenAI and how organizations can implement these technologies in a safe and secure way.
Potential Risks of Generative AI
Let’s look at a few of the risks that are posed by Generative AI as stated by Avivah Litan, VP Analyst at Gartner:
1. Hallucinations
This is a term that refers to incorrect or misleading results that GenAI models can generate which are usually caused by insufficient training data, false assumptions made by the model, or biases in the data used to train the model. As GenAI programs, like chatbots, become increasingly relied upon, it can prove difficult to spot these errors.
2. Deepfakes
These are fake images, videos, and voice recordings that are generated with ill-natured intentions. Deepfakes have been used to spread misleading information, create fake accounts, and even attack celebrities and politicians. This use of GenAI can create significant risk for individuals, organizations, and governments.
3. Data privacy
As technology advances, it becomes increasingly important to take steps to protect our data online. When interacting with AI chatbots, it is easy for employees to expose sensitive data, which can then be stored in the application and possibly used to train other models.
4. Copyright issues
GenAI chatbot models are trained on large amounts of data from the internet, some of which could be copyrighted. This may cause outputs of information that violates copyright or intellectual property protections, even if the user is unaware.
Factors for Increased Risk
There are organizations and even individuals that do not consider the risks of AI tools until they are already in use. Critical drivers of risk in terms of GenAI technologies mostly stem from a lack of knowledge and understanding about what really happens inside of these models. Here are a few of these key points from Gartner that add to the probability of risks:
Most people can’t explain what AI is and does to the managers, users and consumers of AI models: It is crucial to understand what different programs do in regards to their operations, especially if they are being implemented into an organization. Important knowledge to be aware of includes the model’s strengths and weaknesses, its likely behavior, and any potential biases.
Anyone can access ChatGPT and other Generative AI tools: While true that GenAI can transform the way organizations conduct their work, there is also great risk involved, as stated above.
AI models and apps require constant monitoring: These advanced technologies need to be observed and controlled consistently to keep them fair, compliant, and ethical. There should be oversight throughout model and application development, testing and deployment, and ongoing operations.
Managing the Risks
The development and innovation of Generative AI will continue, so it is important for organizations to understand the risks and implement strategies for AI trust, risk, and security management (AI TRiSM). This term was formulated at Gartner as a model to manage the risks associated with GenAI.
“AI trust, risk and security management (AI TRiSM) ensures AI model governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection. This includes solutions and techniques for model interpretability and explainability, AI data protection, model operations and adversarial attack resistance.”
Through AI TRiSM strategies, enterprise leaders can implement policies and practices for safe and trusted usage of GenAI tools. 26% of Gartner survey respondents said they are currently implementing or using privacy-enhancing technologies (PETs), ModelOps (25%) or model monitoring (24%). Aside from these technologies, here are a few ways to help organizations manage the risks of GenAI:
Manually review model outputs: When using GenAI tools like ChatGPT as they are, or an “out-of-the-box” model, it is important to make sure that any results are not incorrect or biased.
Monitor employee use of GenAI tools: It is important to inform employees and create policies to ensure that they do not input information or ask questions in models such as ChatGPT that can expose sensitive information. Consider monitoring unauthorized uses of ChatGPT with security controls to confirm that there are no policy violations.
Thoroughly review trained data: When customizing GenAI tools for company use, the data that the model is trained on is crucial in determining its outputs. Not only is it important to be sure that the data is accurate, but also that it is unbiased.
Consistently oversee GenAI systems: As stated previously, a big mistake that organizations can make is a lack of monitoring of AI models. Keeping humans involved in GenAI implementation and ongoing operations can help ensure accuracy and mitigate hallucinations.
As Generative AI grows in popularity, it is the responsibility of organizations to make sure that they are using these technologies in an ethical and safe manner. Understanding the risks and implementing AI TRiSM strategies can help these businesses effectively utilize GenAI tools in their everyday operations.
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