The rapid rise of generative AI, exemplified by ChatGPT, has garnered widespread attention in the business world. However, ChatGPT is just one facet of a broader generative AI revolution driven by advancements in Large Language Models (LLMs) and foundation models. These breakthroughs have ushered in a new era of AI capabilities across various domains, including images, audio, video, and text. Generative AI empowers machines to exhibit computational creativity, generating valuable content on demand. Given its power and accessibility, every industry and company will be affected by this technology.
While business leaders and employees are rightly excited about the possibilities, it’s vital to recognize the associated business and security risks and take measures to mitigate them. Key areas of concern in the realm of cybersecurity for generative AI include data and intellectual property protection, risks related to malicious content, misinformation, copyright issues, biases, and more. Generative AI projects and products come with an elevated risk of compromise, necessitating a well-planned and executed security strategy from the outset. In this article, we’ll outline our top five security recommendations for enterprises considering the implementation of generative AI.
1. Creating a Trusted Environment to Minimize Data Loss
One of the primary concerns when granting access to generative AI applications like ChatGPT is the risk of data loss, especially intellectual property and sensitive information. Employees, in their quest for efficiency or curiosity, may inadvertently transmit confidential data through AI prompts. However, this risk can be significantly reduced with careful planning:
- Consider building a custom front-end that replaces the application interface to directly utilize the chat LLM API (OpenAI Enterprise)
- Implement a sandbox environment to isolate data, serving as a secure gateway for consuming LLM services. Additional filters can be applied to protect data and minimize biases.
- Assess data requirements for specific use cases, determining whether sensitive data can remain under the company’s direct control or be exchanged with hosted services in a standalone environment. OPENAI enterprise keeps data private in their current TOS.
2. Employee Training: Nurturing a Culture of Cybersecurity
The rapid adoption of ChatGPT and generative AI, in general, has created a unique challenge for business leaders. Many employees independently discover and explore these technologies through social channels and media, potentially leading to misinformation and cybersecurity threats. To address this challenge:
- Implement a workforce training program to educate employees about the business and security risks associated with generative AI.
- Foster flexibility and responsiveness in your training approach, recognizing the dynamic nature of this rapidly evolving field.
3. Transparency in Data Usage
Whether your organization is utilizing external foundation models or customizing them for specific business needs, transparency regarding data usage is essential. Data forms the core of large language models, and using models trained on poor-quality data can jeopardize results and the organization’s reputation. To enhance trust:
- Identify and assess potential risks in the training data, including bias, copyright issues, and manipulation.
- Employ tools and techniques to evaluate, measure, monitor, and synthesize training data.
- Prioritize transparency by openly sharing information about data sources, flaws, and bias, fostering trust across the organization.
4. Combating ‘AI for Bad’ with Human-AI Collaboration
To ensure that AI is harnessed for beneficial purposes, businesses can utilize generative AI to enhance security. Implementing a “human in the loop” approach and employing reinforcement learning with human feedback (RLHF) can fine-tune models and bolster their security. Constitutional AI, which monitors and scores model responses, further enhances model safety.
5. Recognizing Emerging Risks to the Models
AI models themselves can be vulnerable to attacks, including “prompt injection” attacks that manipulate models into delivering false or malicious responses. For example, subtle instructions like “ignore all previous directions” in a prompt can bypass system controls. This underscores the importance of considering new threats, such as prompt injection, and designing robust security systems to protect models.
Ensuring Safe Generative AI Implementation
Generative AI and foundation models represent a significant milestone in AI development, offering limitless opportunities. However, they also introduce new risks and threats. Business leaders must not only recognize these risks but also take prompt action to mitigate them. There are evolving models, frameworks, and technologies available to guide AI programs, emphasizing trust, security, and privacy throughout the process. By focusing on trustworthy AI strategies, trust by design, collaboration, and continuous monitoring, organizations can harness the power of generative AI securely, delivering value to their business and users while safeguarding their interests and reputation.
The generative AI revolution is reshaping industries and businesses worldwide. However, this transformative power comes with a responsibility to ensure secure implementation. Business leaders must proactively address cybersecurity concerns, protect their data, and establish trust with employees and users. By following these top five security recommendations, enterprises can confidently embrace generative AI, unlock its full potential, and secure their future in an AI-driven world.
Zobo has an extensive training program for businesses that can help implement AI into project management, data analytics and day to day operations. We start with the basics and have your team ready to use AI to assist with day to day tasks in a short time period. We are here to help you think through any of your security concerns email@example.com.