Understanding Generative AI Risks for Finance, Healthcare, and More

Introduction to Generative AI 

Generative AI uses advanced technology to create new content like images, text, and videos based on existing data. It is especially useful for tasks that involve conversation or creativity. However, it can produce different results for each interaction, and leaders need to properly manage and control implementations. 

Generative AI can be used for many purposes, such as:  

• Creating reports, articles, and graphics 

• Conducting research 

• Summarizing information 

• Assisting with virtual tasks 

• Translating languages 

• Personalizing experiences 

It’s especially powerful when used as an assistant in human-led workflows.  

Common Risks and Concerns with AI 

A common concern with generative AI is its potential misuse by malicious actors. Generative AI can be used to create fake content, such as deepfake videos, voice recordings, and misinformation, which can be exploited for harmful purposes. It can also be used to create sophisticated phishing or malware schemes that are more challenging to detect. 

Chatbots utilizing generative AI can collect sensitive user data, making it vulnerable to breaches or unauthorized sharing with third parties. Additionally, generative AI responses can sometimes be incorrect, known as hallucinations, or even biased. Users unfamiliar with these limitations might be misled and may have a hard time detecting substandard synthetic content (content created by generative AI). 

Industry-Specific Risks 

Generative AI Risks in Financial Services

In the financial services sector, the reliability and robustness of generative AI are crucial. Even small errors can have significant implications, making it essential to implement controls, especially in high risk or externally facing deployments. The decision-making process of generative AI systems is not transparent, making it challenging to understand how certain outcomes are reached. This lack of transparency can complicate the process of drawing conclusions or making recommendations based on AI-generated results. 

Healthcare Concerns with Generative AI 

In healthcare, generative AI systems can perpetuate or even exacerbate existing biases, potentially worsening disparities in treatment and outcomes. To prevent errors and ensure patient safety, it is critical to have humans involved in the loop. 

Technology Sector Challenges 

The technology sector faces its own set of challenges with generative AI. The complexity of monitoring and managing AI systems can pose operational difficulties, particularly within large organizations. Leveraging large generative AI models like large language models requires substantial computational resources and can result in an increased energy demand. The environmental impact of these activities is an increasing concern. 

Managing AI Risks 

Risk Management Strategies for Generative AI 

To effectively manage risks associated with generative AI, it’s crucial to incorporate human oversight in the AI decision-making process. Also known as the “human in the loop” approach, this helps catch errors and biases. Regularly gathering and incorporating feedback from both internal and external stakeholders is essential for continuous improvement of AI deployments. Conducting regular testing and red-teaming exercises can help to identify and mitigate potential risks, and performing testing on a regular schedule keeps strategies up to date with any changes to commercially available large language models or functionality.  

Implementing AI Governance and Compliance

Creating an environment where cross-functional conversations and decisions can take place often leads to better technology and higher-value outcomes. By leveraging existing data governance and compliance programs and expanding stakeholders to include technology and business specialists, organizations can leverage best practice standards that encourage new ideas and innovation while governing AI use. Many frameworks now exist for AI governance that were not available just a few years ago.  

Best Practices for AI Security  

In addition to following general cybersecurity and data privacy best practices, it’s important to implement comprehensive AI security risk assessment frameworks. These frameworks should cover the entire lifecycle of AI system development and deployment with considerations for third party vendor implementations. Developing and maintaining incident response plans specifically tailored for AI systems ensures a quick and effective response to any security incidents. This proactive approach helps safeguard organizations against potential threats and vulnerabilities. 

Preparing for the Future of AI

Senior leaders should stay informed about the latest trends in capabilities to ensure their organizations remain competitive and secure. It’s crucial to initiate governance processes early, laying a strong foundation for future developments. Additionally, building upon existing cybersecurity and data governance practices is essential. By doing so, organizations can build a robust framework that integrates these practices and mitigates the unique risks that generative AI add to the landscape. Bringing together a cross-functional team will further enhance this effort, ensuring comprehensive and cohesive governance across all departments. Contact us below to discuss how the risks and opportunities presented by generative AI could impact your organization.