Deploying generative artificial intelligence (GenAI) models or large language models (LLMs), such as GPT-4 and others, in a corporate or other organizational environment presents a unique set of challenges, both technical and human-centric. This pair of blogs looks into the different considerations that both the business and the security teams must address to ensure they create an ecosystem for safe, secure LLM deployment across the enterprise.
User Training and Awareness
Every user at every level of the organization who will be interacting with the model must understand both the risks and benefits its use brings to the enterprise, as well as the limitations and capabilities of the model itself. In one sense, LLMs are the gifts that keep on giving when the topic is attack surfaces; every prompt and every response has the capacity to be a vector for trouble. A lack of awareness on the part of even one user could result in unintentional misuse, reliance on inaccurate or misleading outputs, or allowing a malicious actor access to your system.
Ethical Guidelines and Compliance
Ensure the deployment and intended use of the LLM aligns with organizational principles, acceptable use policies, and other corporate considerations, and with ethical industry standards and legal regulations, especially concerning data privacy laws. If users are unaware of the legal obligations—and consequences—of relevant regulations, acting in violation of them is almost a given. Organizations that conduct business activities in Europe or the U.S., or even in specific states, such as California, must follow the controlling governmental regulations, such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), or California Consumer Privacy Act (CCPA), respectively.
Monitoring for Misuse
Understanding how users are intended to interact with the model must be balanced with the capability to ensure they are not misusing their access. Continuously monitoring model usage by individuals and teams can provide valuable insights into the content, costs, and consumption rate of the model’s output. Traceability tracking can also ensure users are complying with acceptable use and other policies, and not using the model to generate deceptive, discriminatory, or harmful content.
Transparency and Explainability
While the model’s logic might be difficult to discern, its operations should be as transparent and easily understood as possible. Maintaining prompt histories that can be traced to the user and include responses, as well as any alerts triggered by either the prompt or the response, can provide longitudinal information about usage or reasoning patterns and trends.
Feedback Loop with Stakeholders
A feedback mechanism for users and other stakeholders to report issues, provide input, request assistance, or suggest improvements is a critical means of engaging them, and can lead to more, as well as more effective, use of the model. Feedback data can be used to continuously improve the model, enhance the user experience, and nurture operational efficiencies made possible by the model.
Taking these human-centric factors into account will allow your organization to more effectively and securely deploy LLMs across the enterprise, while making the experience user-friendly.