The Top Five Risks of Generative AI & How to Mitigate Them

Article written by Tom Woolums

Many organizations are reaping significant benefits from harnessing the power of generative AI tools. From automating routine tasks to accelerating complex decision-making, generative AI is reshaping the technological landscape and accelerating digital transformation. While generative AI has numerous benefits, its rapid integration into daily work processes comes with new risks.

Some organizations believe the risks outweigh the benefits and restrict or ban the use of generative AI tools due to concerns about data security and confidentiality, accuracy and reliability, and ethical issues. However, embracing generative AI and understanding its nuances empowers organizations to minimize the risks and realize the benefits of this transformative technology. Understanding the risks associated with generative AI tools is essential for using them safely and responsibly. This article outlines five risks organizations should consider before using or implementing generative AI tools.

1. Data Privacy Risks

Generative AI tools trained on scraped internet data gather details, such as the user’s IP address, browser version, and interactions with the AI tools, including queries and prompts, content types engaged with, features used, and browsing activities over time and across websites. In many cases, users have little knowledge or control over how their personal data is stored and processed, who can access it, and what security measures are in place to protect it. Their sensitive data may be used to create content that inadvertently reveals private information or violates privacy rights. This content could be accessible to an audience that includes competitors, customers, or malicious actors looking for sensitive data to use for spear-phishing attacks, identity theft, and fraud.

Keeping Your Data Safe

To keep your data safe when using generative AI tools:

• Do not share personal details or sensitive information such as trade secrets, proprietary business information, or other confidential data.
• Carefully read and understand the terms of service and privacy policy. Familiarize yourself with the AI tool’s data usage policy, how it uses input data to improve its models, and its policies for protecting user privacy and confidentiality. Pay particular attention to the tool’s methods of gathering, storing, and using data.
• Sanitize input data by removing any identifying information that could be traced back to you or your organization.

2. Intellectual Property Risks

Generative AI models are trained on extensive datasets, including publicly available text, images, video, music, speech, and software code containing unlicensed content. While these AI tools aim to avoid directly copying licensed content, they don’t guarantee that their responses won’t inadvertently infringe on existing copyrights. Moreover, determining the ownership of AI-generated content can be complex as it’s challenging to distinguish between the user’s input and the AI’s contribution, making the legal status of AI-generated works ambiguous.

Mitigating Intellectual Property Risks

Strategies for addressing intellectual property risks include:

• Use clean training data and establish clear provenance for generated content.
• Consult with legal counsel to assess the risk and ensure compliance with copyright regulations specific to your use case. Generative AI is not a substitute for professional legal advice.
• Establish clear licensing agreements for the use of datasets and AI models.
• Develop guidelines for ethical AI practices that respect the rights of original content creators and avoid generating harmful or infringing content.
• Conduct regular audits of training data and generated content to ensure compliance with IP laws.

3. Misleading or Incorrect Results

Many organizations have faced the consequences of trusting misleading or inaccurate AI output. There are notable instances of misinformation being published by major news outlets, attorneys being fined for using fabricated cases, medical professionals misdiagnosing patient conditions, and substantial losses by clients of financial advisors relying on flawed AI-generated analysis. These cases and more underscore the importance of human oversight and verification when using AI-generated results.

Managing the Risks of Faulty AI Information

Strategies for reducing risks associated with incorrect responses from generative AI systems include:

• Review and edit the generated content. Treat responses as inspiration rather than verbatim content, and use them as a starting point to create your own original work.
• Educate users on the capabilities and limitations of generative AI and provide training on how to interact effectively with and interpret AI responses.
• Incorporate human oversight to review AI responses before they are finalized or acted upon.
• Validate AI-generated responses against known facts or expected outputs and cross-verify with multiple data sources.

4. Biased Results

Biased content produced by generative AI tools can have real-world consequences that significantly impact organizations and individuals. Neglecting ethical considerations can introduce unintended biases into the data, resulting in discriminatory results. Addressing these consequences requires organizations to proactively identify, mitigate, and prevent bias in AI systems to foster fairness, transparency, and accountability in developing and deploying generative AI tools.

Understanding Bias in Generative AI

Bias in AI-generated content often stems from several factors. Human biases can be unintentionally incorporated into AI models during their development. When biased data, such as stereotypes based on race, gender, ethnicity, age, and other factors, is used to train an AI model, it can learn, perpetuate, and potentially magnify these biases. Furthermore, unconscious biases may be reflected in the decisions made during the design and implementation of AI systems. The features and success criteria you select can introduce biases, and certain machine learning algorithms may unintentionally favor certain data over others, resulting in biased outcomes.

Managing Bias in AI-Generated Content

Completely eliminating bias can be challenging, and ongoing vigilance is necessary to manage and reduce the risks associated with biased AI content. Transparency and fairness are essential to mitigate these consequences.

• Use diverse and representative training datasets to train the model.
• Engage diverse teams in the design and development process.
• Maintain transparency in how AI systems make decisions.
• Employ fairness-aware algorithms and techniques.
• Implement tools and techniques for detecting and mitigating biases within the model.
• Regularly audit and test AI systems for bias.

5. Expanding Attack Surface

Generative AI can expand an organization’s attack surface and create new security and privacy risks. To safeguard against expanding attack surfaces, organizations must be vigilant about balancing innovation with robust cybersecurity measures.

Security Implications of Generative AI

Using generative AI tools often requires investment in new data management, storage, and networking infrastructure. More complex infrastructure needs more advanced security measures, which can be difficult to implement, configure, and monitor. Integrating generative AI tools often involves reliance on third-party software and libraries, which can also introduce vulnerabilities. Additionally, many generative AI tools are accessed via APIs, which may have security vulnerabilities that attackers can exploit to compromise the AI system or the data it processes.

Without properly implemented and managed access controls, unauthorized users may gain access to the AI tool or its outputs. Once they have access, adversaries can misuse generative AI models to consume excessive computational resources, leading to denial-of-service (DoS) attacks. They can also inject corrupted data during the training process to introduce weaknesses in the model, resulting in biased or faulty results, reduced performance, and additional security threats.

Addressing the Challenges of Generative AI Tools Increasing the Attack Surface

Effectively planning, implementing, and continuously monitoring generative AI systems is vital to mitigating risks and securing new infrastructure. Strategies for mitigating security risks from the expanded security attack surface associated with generative AI tools include:

• Implementing advanced encryption for data in transit and at rest.
• Implementing comprehensive monitoring and logging to detect and respond to malicious activities targeting the AI system.
• Conducting regular security audits and vulnerability assessments of your generative AI tools and their integration points. Incorporate adversarial training techniques to make AI models more robust against attacks.
• Regularly updating and patching AI tools and their dependencies, including third-party libraries and frameworks, with the latest security patches.
• Implement robust authentication and authorization mechanisms for AI-related tools, APIs, interfaces, and data used in AI training and operations.
• Educate staff about practices for using the tools securely and the potential risks associated with generative AI.

Consider implementing zero trust architecture (ZTA) when deploying generative AI tools. ZTA can significantly enhance the security and reliability of your infrastructure. ZTA’s continuous monitoring and verification help detect and mitigate threats more effectively. ZTA also helps ensure that users and devices accessing the AI tools are authenticated and authorized. ZTA is designed to adapt to changing environments and technologies. As your use of generative AI tools grows, ZTA can scale to meet the security needs of your expanding infrastructure.

Conclusion

From driving creativity and innovation to enhancing productivity and reducing overhead, generative AI offers significant benefits today and tremendous promise for the future. However, its impact on security and confidentiality demands vigilance and careful management. As generative AI technology continues to evolve, organizations need to strike a balance between innovation and risk.

Mitigating the risks of generative AI tools involves implementing strong data governance practices, choosing reputable tool providers, conducting thorough risk assessments, and training users to use generative AI safely and responsibly. Organizations must develop, implement, and clearly communicate guidelines and policies on the appropriate use of generative AI and put the right data compliance and governance tools in place for ongoing enforcement. By proactively addressing these challenges, organizations can reduce the risks and reap the benefits of using generative AI.